research proposal for software engineering

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What are Software Engineer Research Topics?

Software engineer research topics are areas of exploration and study in the rapidly evolving field of software engineering. These research topics include various software development approaches, quality of software, testing of software, maintenance of software, security measures for software, machine learning models in software engineering, DevOps, and architecture of software. Each of these software engineer research topics has distinct problems and opportunities for software engineers to investigate and make major contributions to the field. In short, research topics for software engineering provide possibilities for software engineers to investigate new technologies, approaches, and strategies for developing and managing complex software systems.

For example, research on agile software development could identify the benefits and drawbacks of using agile methodology, as well as develop new techniques for effectively implementing agile practices. Software testing research may explore new testing procedures and tools, as well as assess the efficacy of existing ones. Software quality research may investigate the elements that influence software quality and develop approaches for enhancing software system quality and minimizing the faults and errors. Software metrics are quantitative measures that are used to assess the quality, maintainability, and performance of software.

The research papers on software engineering topics in this specific area could identify novel measures for evaluating software systems or techniques for using metrics to improve the quality of software. The practice of integrating code changes into a common repository and pushing code changes to production in small, periodic batches is known as continuous integration and deployment (CI/CD). This research could investigate the best practices for establishing CI/CD or developing tools and approaches for automating the entire CI/CD process.

List of Software Engineer Research Topics in 2024

Here is a list of Software Engineer research topics:

Artificial Intelligence and Software Engineering
Natural Language Processing
Applications of Data Mining in Software Engineering
Data Modeling
Verification and Validation
Software Project Management
Software Quality
Software Models

1. Artificial Intelligence and Software Engineering

a. Intersections between AI and SE

The creation of AI-powered software engineering tools is one potential research area at the intersection of artificial intelligence (AI) and software engineering. These technologies use AI techniques that include machine learning, natural language processing, and computer vision to help software engineers with a variety of tasks throughout the software development lifecycle. An AI-powered code review tool, for example, may automatically discover potential flaws or security vulnerabilities in code, saving developers a lot of time and lowering the chance of human error. Similarly, an AI-powered testing tool might build test cases and analyze test results automatically to discover areas for improvement.

Furthermore, AI-powered project management tools may aid in the planning and scheduling of projects, resource allocation, and risk management in the project. AI can also be utilized in software maintenance duties such as automatically discovering and correcting defects or providing code refactoring solutions. However, the development of such tools presents significant technical and ethical challenges, such as the necessity of large amounts of high-quality data, the risk of bias present in AI algorithms, and the possibility of AI replacing human jobs. Continuous study in this area is therefore required to ensure that AI-powered software engineering tools are successful, fair, and responsible.

b. Knowledge-based Software Engineering

Another study area that overlaps with AI and software engineering is knowledge-based software engineering (KBSE). KBSE entails creating software systems capable of reasoning about knowledge and applying that knowledge to enhance software development processes. The development of knowledge-based systems that can help software engineers in detecting and addressing complicated problems is one example of KBSE in action. To capture domain-specific knowledge, these systems use knowledge representation techniques such as ontologies, and reasoning algorithms such as logic programming or rule-based systems to derive new knowledge from already existing data.

KBSE can be utilized in the context of AI and software engineering to create intelligent systems capable of learning from past experiences and applying that information to improvise future software development processes. A KBSE system, for example, may be used to generate code based on previous code samples or to recommend code snippets depending on the requirements of a project. Furthermore, KBSE systems could be used to improve the precision and efficiency of software testing and debugging by identifying and prioritizing bugs using knowledge-based techniques. As a result, continued research in this area is critical to ensuring that AI-powered software engineering tools are productive, fair, and responsible.

2. Natural Language Processing

a. Multimodality

Multimodality in Natural Language Processing (NLP) is one of the appealing research ideas for software engineering at the nexus of computer vision, speech recognition, and NLP. The ability of machines to comprehend and generate language from many modalities, such as text, speech, pictures, and video, is referred to as multimodal NLP. The goal of multimodal NLP is to develop systems that can learn from and interpret human communication across several modalities, allowing them to engage with humans in more organic and intuitive ways.

The building of conversational agents or chatbots that can understand and create responses using several modalities is one example of multimodal NLP in action. These agents can analyze text input, voice input, and visual clues to provide more precise and relevant responses, allowing users to have a more natural and seamless conversational experience. Furthermore, multimodal NLP can be used to enhance language translation systems, allowing them to more accurately and effectively translate text, speech, and visual content.

b. Efficiency

The development of multimodal NLP systems must take efficiency into account. as multimodal NLP systems require significant computing power to process and integrate information from multiple modalities, optimizing their efficiency is critical to ensuring that they can operate in real-time and provide users with accurate and timely responses. Developing algorithms that can efficiently evaluate and integrate input from several modalities is one method for improving the efficiency of multimodal NLP systems.

Overall, efficiency is a critical factor in the design of multimodal NLP systems. Researchers can increase the speed, precision, and scalability of these systems by inventing efficient algorithms, pre-processing approaches, and hardware architectures, allowing them to run successfully and offer real-time replies to consumers. Software Engineering training will help you level up your career and gear up to land you a job in the top product companies as a skilled Software Engineer.

3. Applications of Data Mining in Software Engineering

a. Mining Software Engineering Data

The mining of software engineering data is one of the significant research paper topics for software engineering, involving the application of data mining techniques to extract insights from enormous datasets that are generated during software development processes. The purpose of mining software engineering data is to uncover patterns, trends, and various relationships that can inform software development practices, increase software product quality, and improve software development process efficiency.

Mining software engineering data, despite its potential benefits, has various obstacles, including the quality of data, scalability, and privacy of data. Continuous research in this area is required to develop more effective data mining techniques and tools, as well as methods for ensuring data privacy and security, to address these challenges. By tackling these issues, mining software engineering data can continue to promote many positive aspects in software development practices and the overall quality of product.

b. Clustering and Text Mining

Clustering is a data mining approach that is used to group comparable items or data points based on their features or characteristics. Clustering can be used to detect patterns and correlations between different components of software, such as classes, methods, and modules, in the context of software engineering data.

On the other hand, text mining is a method of data mining that is used to extract valuable information from unstructured text data such as software manuals, code comments, and bug reports. Text mining can be applied in the context of software engineering data to find patterns and trends in software development processes

4. Data Modeling

Data modeling is an important area of research paper topics in software engineering study, especially in the context of the design of databases and their management. It involves developing a conceptual model of the data that a system will need to store, organize, and manage, as well as establishing the relationships between various data pieces. One important goal of data modeling in software engineering research is to make sure that the database schema precisely matches the system's and its users' requirements. Working closely with stakeholders to understand their needs and identify the data items that are most essential to them is necessary.

5. Verification and Validation

Verification and validation are significant research project ideas for software engineering research because they help us to ensure that software systems are correctly built and suit the needs of their users. While most of the time, these terms are frequently used interchangeably, they refer to distinct stages of the software development process. The process of ensuring that a software system fits its specifications and needs is referred to as verification. This involves testing the system to confirm that it behaves as planned and satisfies the functional and performance specifications. In contrast, validation is the process of ensuring that a software system fulfils the needs of its users and stakeholders.

This includes ensuring that the system serves its intended function and meets the requirements of its users. Verification and validation are key components of the software development process in software engineering research. Researchers can help to improve the functionality and dependability of software systems, minimize the chance of faults and mistakes, and ultimately develop better software products for their consumers by verifying that software systems are designed correctly and that they satisfy the needs of their users.

6. Software Project Management

Software project management is an important component of software engineering research because it comprises the planning, organization, and control of resources and activities to guarantee that software projects are finished on time, within budget, and to the needed quality standards. One of the key purposes of software project management in research is to guarantee that the project's stakeholders, such as users, clients, and sponsors, are satisfied with their needs. This includes defining the project's requirements, scope, and goals, as well as identifying potential risks and restrictions to the project's success.

7. Software Quality

The quality of a software product is defined as how well it fits in with its criteria, how well it performs its intended functions, and meets the needs of its consumers. It includes features such as dependability, usability, maintainability, effectiveness, and security, among others. Software quality is a prominent and essential research topic in software engineering. Researchers are working to provide methodologies, strategies, and tools for evaluating and improving software quality, as well as forecasting and preventing software faults and defects. Overall, software quality research is a large and interdisciplinary field that combines computer science, engineering, and statistics. Its mission is to increase the reliability, accessibility, and overall quality of software products and systems, thereby benefiting both software developers and end consumers.

8. Ontology

Ontology is a formal specification of a conception of a domain used in computer science to allow knowledge sharing and reuse. Ontology is a popular and essential area of study in the context of software engineering research. The construction of ontologies for specific domains or application areas could be a research topic in ontology for software engineering. For example, a researcher may create an ontology for the field of e-commerce to give common knowledge and terminology to software developers as well as stakeholders in that domain. The integration of several ontologies is another intriguing study topic in ontology for software engineering. As the number of ontologies generated for various domains and applications grows, there is an increasing need to integrate them in order to enable interoperability and reuse.

9. Software Models

In general, a software model acts as an abstract representation of a software system or its components. Software models can be used to help software developers, different stakeholders, and users communicate more effectively, as well as to properly evaluate, design, test, and maintain software systems. The development and evaluation of modeling languages and notations is one research example connected to software models. Researchers, for example, may evaluate the usefulness and efficiency of various modeling languages, such as UML or BPMN, for various software development activities or domains.

Researchers could also look into using software models for software testing and verification. They may investigate how models might be used to produce test cases or to do model checking, a formal technique for ensuring the correctness of software systems. They may also examine the use of models for monitoring at runtime and software system adaptation.

The Software Development Life Cycle (SDLC) is a software engineering process for planning, designing, developing, testing, and deploying software systems. SDLC is an important research issue in software engineering since it is used to manage software projects and ensure the quality of the resultant software products by software developers and project managers. The development and evaluation of novel software development processes is one SDLC-related research topic. SDLC research also includes the creation and evaluation of different software project management tools and practices.

Researchers may also check the implementation of SDLC in specific sectors or applications. They may, for example, investigate the use of SDLC in the development of systems that are more safety-critical, such as medical equipment or aviation systems, and develop new processes or tools to ensure the safety and reliability of these systems. They may also look into using SDLC to design software systems in new sectors like the Internet of Things or in blockchain technology.

Why is Software Engineering Required?

Software engineering is necessary because it gives a systematic way to developing, designing, and maintaining reliable, efficient, and scalable software. As software systems have become more complicated over time, software engineering has become a vital discipline to ensure that software is produced in a way that is fully compatible with end-user needs, reliable, and long-term maintainable.

When the cost of software development is considered, software engineering becomes even more important. Without a disciplined strategy, developing software can result in overinflated costs, delays, and a higher probability of errors that require costly adjustments later. Furthermore, software engineering can help reduce the long-term maintenance costs that occur by ensuring that software is designed to be easy to maintain and modify. This can save money in the long run by lowering the number of resources and time needed to make software changes as needed.

2. Scalability

Scalability is an essential factor in software development, especially for programs that have to manage enormous amounts of data or an increasing number of users. Software engineering provides a foundation for creating scalable software that can evolve over time. The capacity to deploy software to diverse contexts, such as cloud-based platforms or distributed systems, is another facet of scalability. Software engineering can assist in ensuring that software is built to be readily deployed and adjusted for various environments, resulting in increased flexibility and scalability.

3. Large Software

Developers can break down huge software systems into smaller, simpler parts using software engineering concepts, making the whole system easier to maintain. This can help to reduce the software's complexity and makes it easier to maintain the system over time. Furthermore, software engineering can aid in the development of large software systems in a modular fashion, with each module doing a specific function or set of functions. This makes it easier to push new features or functionality to the product without causing disruptions to the existing codebase.

4. Dynamic Nature

Developers can utilize software engineering techniques to create dynamic content that is modular and easily modifiable when user requirements change. This can enable adding new features or functionality to dynamic content easier without disturbing the existing codebase. Another factor to consider for dynamic content is security. Software engineering can assist in ensuring that dynamic content is generated in a secure manner that protects user data and information.

5. Better Quality Management

An organized method of quality management in software development is provided by software engineering. Developers may ensure that software is conceived, produced, and maintained in a way that fulfills quality requirements and provides value to users by adhering to software engineering principles. Requirement management is one component of quality management in software engineering. Testing and validation are another part of quality control in software engineering. Developers may verify that their software satisfies its requirements and is error-free by using an organized approach to testing.

In conclusion, the subject of software engineering provides a diverse set of research topics with the ability to progress the discipline while enhancing software development and maintenance procedures. This article has dived deep into various research topics in software engineering for masters and research topics for software engineering students such as software testing and validation, software security, artificial intelligence, Natural Language Processing, software project management, machine learning, Data Mining, etc. as research subjects. Software engineering researchers have an interesting chance to explore these and other research subjects and contribute to the development of creative solutions that can improve software quality, dependability, security, and scalability.

Researchers may make important contributions to the area of software engineering and help tackle some of the most serious difficulties confronting software development and maintenance by staying updated with the latest research trends and technologies. As software grows more important in business and daily life, there is a greater demand for current research topics in software engineering into new software engineering processes and techniques. Software engineering researchers can assist in shaping the future of software creation and maintenance through their research, ensuring that software stays dependable, safe, reliable and efficient in an ever-changing technological context. KnowledgeHut’s top Programming certification course will help you leverage online programming courses from expert trainers.

Frequently Asked Questions (FAQs)

To find a research topic in software engineering, you can review recent papers and conference proceedings, talk to different experts in the field, and evaluate your own interests and experience. You can use a combination of these approaches.

You should study software development processes, various programming languages and their frameworks, software testing and quality assurance, software architecture, various design patterns that are currently being used, and software project management as a software engineering student.

Empirical research, experimental research, surveys, case studies, and literature reviews are all types of research in software engineering. Each sort of study has advantages and disadvantages, and the research method chosen is determined by the research objective, resources, and available data.

Eshaan Pandey

Eshaan is a Full Stack web developer skilled in MERN stack. He is a quick learner and has the ability to adapt quickly with respect to projects and technologies assigned to him. He has also worked previously on UI/UX web projects and delivered successfully. Eshaan has worked as an SDE Intern at Frazor for a span of 2 months. He has also worked as a Technical Blog Writer at KnowledgeHut upGrad writing articles on various technical topics.

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Proposal for master thesis in software engineering

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Software Engineering & Evolution

Software engineering is the most technical part of technical computer science , focused on developing and applying systematic principles common to other kinds of engineering (like mechanical or electrical engineering) to development of software systems. In particular, it covers:

Software requirements , their elicitation, specification and verification
Software architecture , modelling and design
Software testing , quality and guarantees
Software construction , methodologies and processes
Software deployment , configuration and maintenance
Software project management , planning and audit
Software ethics , licensing and collaboration

Software evolution , in particular, is a branch of software engineering focused on studying existing software and not necessarily creating new one. It covers, among other topics:

Mining repositories of versioned source code for interesting facts about software history
Refactoring, restructuring and replatforming of software
Developing and calculating metrics on software artefacts
Automatically measuring quality of source code by detecting harmful constructs
Bug fixing and other forms of corrective maintenance
Automating and tool-supporting maintenance activities

A typical research project in software engineering involves an implementation of a software system at least up to a fully functioning prototype , performing a feasibility study and/or a user study . A typical software evolution project covers development of a software system that analyses or transforms another software system. Both often use methodologies from empirical software engineering .

Prerequisites

Basic knowledge about programming and design of software

Related courses

Model-Driven Engineering
Principles of Programming, Processes and Patterns
Software Evolution

Available Project Proposals

If you are interested in the general topic of Software Engineering and Evolution , or if have your own project idea related to the topic, please contact us directly. Alternatively, you can also work on one of the following concrete project proposals:

Supervisors: Iman Hemati Moghadam , Vadim Zaytsev .

Gaining insights into the patterns of previously applied refactorings can greatly enhance the accuracy of detecting applied refactorings. By utilizing machine learning algorithms, we can extract change patterns through model training on pre-existing refactorings. However, in order to achieve a highly accurate model, it is crucial to train it on a comprehensive and extensive dataset.

The first objective of this project is to create an automated mechanism that generates a dataset of refactorings applied in Java applications. Mauricio et al. [1] offer a dataset with millions of refactorings and an ML model trained on this dataset to recognize refactoring opportunities. The provided dataset includes class, method, and variable-level refactorings but lacks field-level refactorings. Furthermore, the dataset's refactorings are identified using an outdated version of RefactoringMiner [2], which may have missed some applied refactorings. Although RefactoringMiner has improved recall in its latest version, our new experiments [3] revealed still some applied refactorings not detected by its latest version. Consequently, the dataset by Mauricio et al. does not encompass all applied refactorings. Furthermore, in order to develop a model that can effectively identify applied refactoring, we need a more informative dataset that incorporates information from both the original and refactored versions of the program. Currently, the dataset provided by Mauricio et al. only includes information about the original version of the program. Therefore, we need to create the dataset from scratch. The most challenging aspect of this process lies in accurately assessing the validity of each refactoring included in the dataset.

Our approach involves employing RefactoringMiner [2] and RefDetect [3] to extract the refactorings that have been applied in Java applications within the dataset compiled by Mauricio et al. [1]. RefactoringMiner demonstrates high precision, ensuring that the refactorings identified by it can be considered valid. However, its recall rate is comparatively lower than that of RefDetect, which we will employ to identify refactoring instances missed by RefactoringMiner. Given RefDetect's strong recall performance, we are confident that any changes not recognized as refactorings by RefDetect can be classified as non-refactoring modifications. Nevertheless, it is imperative to verify changes identified by RefDetect as refactoring and detected as non-refactoring by RefactoringMiner. We call them ambiguous changes. Although it is not impossible to conduct a manual evaluation, it can be a time-consuming and error-prone task. Therefore, it is imperative to discover an automated approach for validating ambiguous changes. To address this, we propose exploring the possibility of automatically labelling the refactorings using a machine-learning algorithm or a combination of machine-learning and search-based techniques. Further information regarding the challenges encountered and the corresponding approaches proposed to address them is provided in a detailed description of the project.

After establishing the aforementioned dataset, the next objective of this project is to leverage the dataset to train a model that can proficiently recognize applied refactorings in Java applications or augment RefDetect [3] in making more informed determinations when identifying refactorings.

[1] Aniche, M., Maziero, E., Durelli, R., & Durelli, V. H. (2020). The effectiveness of supervised machine learning algorithms in predicting software refactoring . IEEE Transactions on Software Engineering, 48(4), 1432-1450.

[2] Tsantalis, N., Ketkar, A., & Dig, D. (2020). RefactoringMiner 2.0 . IEEE Transactions on Software Engineering, 48(3), 930-950.

[3] Moghadam, I. H., Cinnéide, M. Ó., Zarepour, F., & Jahanmir, M. A. (2021). RefDetect: A multi-language refactoring detection tool based on string alignment . IEEE Access, 9, 86698-86727.

There exist many pre-trained code embedding methods, and determining the most resilient approach in the presence of modifications has consistently intrigued researchers. Although certain investigations have examined and compared a selection of prevalent embedding models such as CodeBERT, Codex, Code2vec, and Code2src, their emphasis has predominantly centred around the generation of adversarial samples via the renaming of entities using Rename refactoring. In a few research works, however, code modifications were made using transformations like loop exchange, try-catch insertion, replacing switch statements with if, swapping unrelated statements, etc.

However, in the majority of software development activities where the pre-trained models are used (such as code completion, code understanding, bug detection and fixing and refactoring assistance, etc.), the modifications extend beyond simple changes. Therefore, it is necessary to evaluate the robustness of the existing pre-trained models on complex code modification. To accomplish this goal, as an initial step, we generate semantically equivalent code samples by applying refactorings such as Extract and Inline Method refactorings and subsequently evaluating the performance of different models. The findings from this research can offer invaluable insights for selecting a robust model for future projects, thereby enhancing decision-making processes.

Supervisor: Fernando Castor

The Rust programming language aims to make systems programming efficient and safe at the same time by helping developers build programs that are safe by construction. The language is statically typed and supports safe access to memory, without the need for a garbage collector or runtime system, with the help of its compiler. It also provides scoped concurrency while avoiding state sharing, with exit synchronization for groups of threads. According to the 2023 StackOverflow developers survey (https://survey.stackoverflow.co/2023/), it is the most admired technology for survey respondents and has been so for many years.

One thing that Rust does not have, though, is a specific mechanism for signaling and handling errors, differently from a number of popular programming languages, such as Java, C++, Swift, and Python. In Rust, unrecoverable errors are signaled by the panic() function. Computations that may produce errors are represented by values of Result, an enumerated type that encapsulates both correct and erroneous results. These values are just regular Rust values and are not propagated automatically, differently from exceptions in other languages. On the one hand, this means that Rust avoids additional runtime infrastructure to perform stack unwinding during exception propagation. On the other hand, developers must explicitly worry about whether the output of a function is an error or not.

Previous work has shown that, in a number of languages, developers give less attention to code that handles errors than to other parts of the code. They test error handling code less [1], capture errors without doing anything with them [2,3], capture the incorrect errors [4], fail to account for potential errors [5], and sometimes simply do not use the language's error handling mechanism [6]. Problemas with error handling are commonplace even in languages that do not include specific mechanisms for handling errors [7].

In this project we would like to address a high-level research question:

RQ How do Rust programmers handle errors? How much code is dedicated to that?

This question can be decomposed in a number of more specific research questions:

RQ1 How are errors typically handled in Rust programs? Are they often ignored, as in other languages?

RQ1 .1 Is it common to have long sequences (in terms of method calls) where we have chained error handling, the kind of thing that would not be there with exception propagation?

RQ2 What do developers think of Rust error handling? Is it better than C? Better than exceptions?

RQ3 Do automated tests for Rust programs test exceptional paths?

RQ4 What are error handling bugs in Rust like?

RQ5 How are errors handled in the presence of scoped concurrency?

[1] Felipe Ebert, Fernando Castor, Alexander Serebrenik. An exploratory study on exception handling bugs in Java programs. J. Syst. Softw. 106: 82-101 (2015)

[2] Nathan Cassee, Gustavo Pinto, Fernando Castor, Alexander Serebrenik. How swift developers handle errors. MSR 2018: 292-302

[3] Bruno Cabral, Paulo Marques. Exception Handling: A Field Study in Java and .NET. ECOOP 2007: 151-175

[4] Nélio Cacho, Thiago César, Thomas Filipe, Eliezio Soares, Arthur Cassio, Rafael Souza, Israel García, Eiji Adachi Barbosa, Alessandro Garcia. Trading robustness for maintainability: an empirical study of evolving c# programs. ICSE 2014: 584-595

[5] Juliana Oliveira, Deise Borges, Thaisa Silva, Nélio Cacho, Fernando Castor. Do android developers neglect error handling? a maintenance-Centric study on the relationship between android abstractions and uncaught exceptions. J. Syst. Softw. 136: 1-18 (2018)

[6] Rodrigo Bonifácio, Fausto Carvalho, Guilherme Novaes Ramos, Uirá Kulesza, Roberta Coelho. The use of C++ exception handling constructs: A comprehensive study. SCAM 2015: 21-30

[7] Magiel Bruntink, Arie van Deursen, Tom Tourwé. Discovering faults in idiom-based exception handling. ICSE 2006: 242-251

Supervisors: Uraz Odyurt , Vadim Zaytsev .

The role of simulation and synthetic data generation for High-Energy Physics (HEP) research is profound. While there are physics-accurate simulation frameworks available to provide the most realistic data syntheses, these tools are computationally demanding. Additionally, the output from physics-accurate simulations is hard to comprehend, hard to manipulate and difficult to work with, as the data is rather close to the real case. These simulations consider accurate models of real-world detectors, which is another limitation.

Parametric and complexity-aware simulation frameworks on the other hand, can redefine the complexity space in drastically simplified terms and generate complexity-reduced data sets. It is also possible to consider a variety of detector models for these simulations. The applications of complexity-reduced simulations and data are numerous. We will be focusing on the role of such data as an enabler for Machine Learning (ML) model design research.

This project aims to extend an existing REDVID simulation framework through addition of new features.

( Read the full project description )

Nowadays, thanks to the rapid proliferation of mobile phones, tablets, and unwired devices in general, energy efficiency is becoming a key software design consideration where the energy consumption is closely related to battery lifetime. It is also of increasing interest in the non-mobile arena, such as data centers and desktop environments. Energy-efficient solutions are highly sought after across the compute stack, with more established results through innovations in hardware/architecture [1,2], operating systems [3], and runtime systems [4]. In recent years, there is a growing interest in studying energy consumption from higher layers of the compute stack and most of these studies focus on application software [5,6,7,8]. These approaches complement prior hardware/OS-centric solutions, so that improvements at the hardware/OS level are not cancelled out at the application level, e.g., due to misuses of language/library/application features.

We believe a critical dimension to further improve energy efficiency of software systems is to understand how software developers think. The needs of developers and the challenges they face may help energy-efficiency researchers stay focused on the real-world problems. The collective wisdom shared by developers may serve as a practical guide for future energy- aware and energy-efficient software development. The conceptually incorrect views they hold may inspire educators to develop more state-of-the-art curricula.

The goal of this work is to obtain a deeper understanding of (i) whether application programmers are interested in software energy consumption, and, if so, (ii) how they are dealing with energy consumption issues. Specifically, the questions we are trying to answer are:

RQ1 What are the distinctive characteristics of energy-related questions?

RQ2 What are the most common energy-related problems faced by software developers?

RQ3 According to developers, what are the main causes for software energy consumption?

RQ4 What solutions do developers employ or recommend to save energy?

We leverage data from StackOverflow, the most popular software development Q&A website, and on issues reported in issue trackers of real open source software projects to answer these questions.

[1] L. Bircher and L. John. Analysis of dynamic power management on multi-core processors. In ICS, 2008.

[2] A. Iyer and D. Marculescu. Power efficiency of voltage scaling in multiple clock, multiple voltage cores. In ICCAD, 2002.

[3] R. Ge, X. Feng, W. chun Feng, and K. Cameron. Cpu miser: A performance-directed, run-time system for power-aware clusters. In ICPP, 2007.

[4] H. Ribic and Y. D. Liu. Energy-efficient work-stealing language runtimes. In ASPLOS, 2014.

[5] Wellington Oliveira, Bernardo Moraes, Fernando Castor, João Paulo Fernandes. Analyzing the Resource Usage Overhead of Mobile App Development Frameworks. EASE 2023: 152-161

[6] Wellington Oliveira, Renato Oliveira, Fernando Castor, Gustavo Pinto, João Paulo Fernandes. Improving energy-efficiency by recommending Java collections. Empir. Softw. Eng. 26(3): 55 (2021)

[7] Ding Li, Shuai Hao, William G. J. Halfond, Ramesh Govindan. Calculating source line level energy information for Android applications. ISSTA 2013: 78-89

[8] Stefanos Georgiou, Maria Kechagia, Tushar Sharma, Federica Sarro, Ying Zou. Green AI: Do Deep Learning Frameworks Have Different Costs? ICSE 2022: 1082-1094

Supervisors: Iman Hemati Moghadam , Vadim Zaytsev .

Metric-based approaches are the most used technique in identifying refactoring opportunities by calculating a particular set of code metrics and applying predefined thresholds for each code metric. However, this approach faces three key challenges. Firstly, the lack of a universally accepted methodology for selecting metrics. Secondly, the absence of standardized definitions for code metrics. Furthermore, the accuracy of the metric-based approaches is heavily dependent on choosing appropriate thresholds.

Machine learning techniques provide effective solutions to overcome the aforementioned limitations. In certain approaches, the code snippet is transformed into a vector of source code metrics, which is then used to train ML classifiers. However, this approach fails to preserve the semantics and structure of the code. Conversely, pre-trained language models like CodeBERT and Codex have been trained in vast amounts of code and have learned to understand the syntax, semantics, and context of programming languages. This valuable knowledge can be transferred in recovering refactoring opportunities, and enable us to perform well even with limited task-specific training data.

The primary objective of this project is to improve the performance of a pre-trained language model (e.g., Codex, CodeBERT, etc.) in identifying opportunities for refactoring within Java applications. To accomplish this, we will start by fine-tuning the chosen language model using Mauricio et al.'s existing dataset [1]. Then, we will utilize the enhanced model to identify potential refactoring opportunities in Java applications. To assess the effectiveness of our approach, we will compare its accuracy with the model proposed by Mauricio et al. [1].

While current machine learning models, such as one developed by Mauricio et al. [1], can identify opportunities for refactorings, they do not provide specific guidance on how to apply the refactorings. For instance, while the existing models can recognize the necessity of applying an Extract Method refactoring to a given method, they do not specify which portion of the method should be extracted (e.g., the initial token and end token).

The goal of this project is to develop algorithms that not only identify parts of code that require refactorings but also recommend appropriate refactoring techniques. The focus of this project will be on two commonly used refactoring types: Extract Method and Inline Method. These two refactoring types are frequently employed by developers, and manually applying them is challenging.

Our proposed approach involves leveraging state-of-the-art deep learning models specifically designed for code analysis, including GraphCodeBERT, CuBERT, CodeGPT, Codex, etc. These models have undergone pre-training on extensive datasets, enabling them to comprehend the syntax, semantics, and context of programming languages effectively. Moreover, they offer the potential for fine-tuning based on our approach. However, before fine-tuning the chosen model, we need to construct a dataset that comprises both pre-refactoring and post-refactoring code snippets. To accomplish this, we will extend Mauricio et al.'s dataset [1], which encompasses thousands of Java applications. The resulting dataset comprises code snippets both before and after the application of refactorings, (i.e., Extract Method and Inline Method refactorings), and will be employed in the fine-tuning process of the selected pre-trained model.

Research scholar program

The Research Scholar Program aims to support early-career professors who are pursuing research in fields relevant to Google.

The Research Scholar Program provides unrestricted gifts to support research at institutions around the world, and is focused on funding world-class research conducted by early-career professors.

Program details

Application status, award information, eligibility criteria, funding amounts, supporting cutting-edge research, award recipients.

Applications are currently closed.

Decisions for the November 2023 application will be announced via email by April 2024. Please check back in Fall 2024 for details on future application cycles.

We encourage submissions from professors globally who are teaching at universities and meet the eligibility requirements. It is our hope that this program will help develop collaborations with new professors and encourage the formation of long-term relationships.

Awards are disbursed as unrestricted gifts to the university and are not intended for overhead or indirect costs. They are intended for use during the academic year in which the award is provided to support the professor’s research efforts.

Post doctoral staff can only serve as a co-PI, not a primary PI.
We consider exceptions for applicants who have been teaching seven years or fewer and had delays, such as working in industry, parental leave, leave of absence, etc. This exception request can be documented on the application.
Faculty can only serve as a PI or Co-PI per round. Applicants cannot serve on two separate proposals.
Applicants can apply a maximum of 3 times within the 7 years post-PhD.

The funds granted will be up to $60,000 USD and are intended to support the advancement of the professor’s research.

Algorithms and optimization

Our team conducts research in graph mining, optimization, operations research, and market algorithms to improve Google's infrastructure, machine learning, and marketplaces. We collaborate with teams across Google and perform research in related areas, such as algorithmic foundations of machine learning, distributed optimization, economics, and data mining.

Health research

Google Health research aims to advance AI and technology to help people live healthier lives through collaborative research with public officials, clinicians, and consumers. We are developing tools to understand population health, novel algorithms to better understand and use complex medical data, and technology to help people find high-quality health information and understand their health status.

We invite proposals that will generate and understand large datasets to improve population health, develop novel algorithms for better understanding of complex medical data, and develop novel methods to extract health insights cheaper, faster, or better.

Machine learning and data mining

Machine learning is the foundation of Google's research, with a broad scope that includes foundational and algorithmic work, critical real-world applications, and topics, such as federated learning, information retrieval, learning theory, optimization, reinforcement learning, robotics, and recommender systems.

Natural language processing

Our team comprises multiple research groups working on a wide range of natural language understanding and generation projects. Our researchers are focused on advancing the state of the art in natural language technologies and accelerating adoption everywhere for the benefit of the user. Natural language processing and understanding plays a major role in driving Google’s company-wide OKRs as language understanding is the key to unlocking Google’s approach: “Build a more helpful Google for everyone that increases the world’s knowledge, success, health, and happiness.”

Quantum computing

The Quantum AI team is developing an error-corrected quantum computer and discovering valuable applications by offering a quantum computing service. We collaborate with academic partners to advance both goals, so if you have a quantum algorithm you would like to run on our service, please submit a proposal.

Software engineering and programming languages

Research on all aspects of software development, including the engineers and the programming languages, libraries, development tools, and processes that they use.

Fundamental and applied science

Large language, visual, and multimodal models have made significant advances in recent years, opening up new possibilities for scientific research. We invite proposals in these four areas:

Applications : Proposals that demonstrate how large language models can be used to advance scientific discovery in a specific field.
Foundations : Proposals that explore broad advances in building, tuning, or deploying large models for scientific research, such as integrating language models with specialized scientific tools, developing multimodal models for understanding scientific data, and accelerating scientific analysis, experimentation, and summarization.
Evaluation : Proposals that develop datasets or methods for benchmarking and evaluating large models for science, including evaluating domain-specific knowledge, assessing factuality and grounding, evaluating multimodal capabilities, and developing tasks that require multi-step scientific reasoning.
HCI : Proposals that enhance scientific workflows, such as automating complex simulation pipelines, with large language models and human-in-the-loop interaction.

Human-computer interaction

HCI researchers at Google design and build large-scale interactive systems that aim to be humane, simple-to-understand, and delightful to use. We work across a variety of HCI disciplines, including predictive and intelligent user interfaces, mobile and ubiquitous computing, social and collaborative computing, and interactive visualization.

Machine perception

Machine perception researchers at Google develop algorithms and systems to tackle a wide range of tasks, including action recognition, object recognition and detection, hand-writing recognition, audio understanding, perceptual similarity measures, and image and video compression.

Google's privacy research reaches across multiple teams, focusing on different aspects of privacy to advance the state of the art and develop tools to protect users and give them control over their data. This includes work on privacy-preserving technologies using cryptography and differential privacy, machine learning for privacy, user interface design and human-computer interactions to make communication clear and empower users, privacy policy to define Google's guiding principles for user protection, and system analysis and measurement to develop techniques to evaluate the privacy health of Google's systems.

Google's security and anti-abuse research team brings together experts from multiple disciplines to defend users from a wide range of threats. This includes work on access control, information security, networking, operating systems, language design, cryptography, fraud detection, machine learning for abuse detection, denial of service, emerging threats, user interfaces, and other human-centered aspects of security.

Systems and networking systems

Google's systems and networking systems research is focused on building and deploying novel systems at unprecedented scale. Our work spans the entire spectrum of computing, from large-scale distributed systems to individual machines to accelerator technologies.

We address fundamental questions around data center architecture, cloud virtual networking, wide-area network interconnects, software-defined networking, machine learning for networking, large-scale management infrastructure, congestion control, bandwidth management, capacity planning, and designing networks to meet traffic demands.

What is the evaluation criteria when assessing proposals?

To ensure fairness, we use a scoring rubric for consistency across reviews. We look at the criteria below to assess proposals. Proposals must comply with the required format and other Research Scholar Program guidelines.

Faculty Merit : Faculty is accomplished in research, community engagement, and open source contributions, with potential to contribute to responsible innovation.
Research Merit : Faculty's proposed research is aligned with Google Research interests, innovative, and likely to have a significant impact on the field.
Proposal Quality : The research proposal is clear, focused, and well-organized, and it demonstrates the team's ability to successfully execute the research and achieve a significant impact.
Broadening Participation : Faculty is committed to broadening participation in computing through their work on a variety of initiatives, including, for example, designing and deploying programs, and training and mentoring students from historically marginalized groups.
AI Ethics Principles : The research proposal strongly aligns with Google's AI Principles .

What are the steps for the selection process?

November: Applications open
December/January: Proposal reviews and scoring
February: Committee reviews proposals, scoring and make selections
March: Approval process for selected proposals
April: Applicants are notified of decision

We completely understand the desire to receive feedback and do our best to meet this request. However, due to the high volume of applications received, you may not receive feedback on your proposal.

To ensure fairness, we use a scoring rubric for consistency across reviews.

How many times can I apply for the Research Scholar program?

Faculty may apply up to a maximum of 3 times within the 7 years they received their PhD.

Can I receive this award more than once?

Faculty can receive a Research Scholar award only once. Previous Faculty Research Award recipients are still eligible to receive a Research Scholar award.

Who is eligible to apply for the Research Scholar Program?

Institutions:

We accept applications from full-time faculty at universities around the world. Funding is focused on supporting the faculty’s research. We do not allow applications from non-degree-granting research institutions.
Since our funding is structured as unrestricted gifts to degree-granting Universities, we cannot process awards to other institutions (e.g. not-for-profits institutions, hospitals, non-degree-granting research institutes, etc) even if they are affiliated with a University. A Principal Investigator must apply in his or her capacity as a university professor and must be able to accept an award through that University.

Principal Investigator Requirements:

Global faculty who have received their PhD less than 7 years from submission from degree-granting institutions who are doing research within fields relevant to Google.
An applicant may only serve as Principal Investigator or co-Principal Investigator on one proposal per round, they cannot be listed on two separate proposals.
We understand that titles may differ globally. In order for someone without the title of professor to apply, he or she must be a full-time faculty member at an eligible institution and serve as a formal advisor to masters or PhD students. We may, at our discretion, provide funding for Principal Investigators who advise undergraduate students at colleges that do not award advanced degrees.

Past Applicants:

If an applicant’s proposal was not selected for funding the previous round, they are welcome to apply with a new proposal (or substantively revised proposal) the following round. A Principal Investigator can apply a maximum of 3 times within the 7 years post-PhD.

How do I apply for the Research Scholar Program?

The application process includes filling out an online form requesting basic information and uploading a PDF proposal via the form. As part of the online form, you will be asked to select a topic area. Please select carefully, as this will help us in ensuring your proposal is read by the appropriate reviewers. Do not send any confidential or proprietary information in your proposal. Any information you send us as part of your application will be considered not confidential regardless of any markings or designations on it.

I have a social science background, can I still apply?

Yes. We focus on funding social science research that looks at technology's implications and impacts on individuals and society. We typically review submissions from fields like human-computer interaction, psychology, and science and technology studies, as well as research in computer science fields with a strong emphasis on the human experience.

What is the proper format for a Research Scholar proposal?

The proposal should be a maximum of 5 pages if you are a sole Principal Investigator.
If you choose not to include the co-Principal Investigator’s CV then your proposal should only be 5 pages.
The extra 2 pages will only accommodate for an additional CV, not for additional proposal content.
The maximum page limit includes the 2-page CV of the primary Principal Investigator, which is required for all applications (again a 2-page CV for a co-Principal Investigator is optional).
To be fair to you and others, we do not consider proposals longer than the maximum page limit.
We request a Google Scholar profile link as part of the online application form. Our reviewers find it helpful to be able to easily reference a Principal Investigator's publication history to see how the current proposal relates to past work the Principal Investigator has done in relevant fields. The Google Scholar profile complements, but does not replace, the Principal Investigator's 2-page CV.
We do not require a budget breakdown since we have flat funding amounts we will grant based on region.
We would prefer proposals to respect a minimum 10pt font size and 1-inch (2.5-cm) margins. Our reviewers value readability.
Below is an example of what a proposal may look like (though the relative length of each section may differ by proposal).

Proposal Format

Proposal Title
Principal Investigator full name, contact information (postal address, email address, phone), affiliation (university, school, college and/or department)
Research goals, including a problem statement.
Description of the work you'd like to do, as well as the expected outcomes and results.
How this relates to prior work in the area (including your own, if relevant)
References, where applicable.
Our goal is to support work where the output will be made available to the broader research community. To that end, we ask that you provide us with a few sentences sharing what you intend to do with the output of your project (e.g. open sourcing code, making data sets public, etc.). Please note that the awards are structured as unrestricted gifts, so there are no legal requirements once a project is selected for funding. This is simply a statement of your current intentions.
The maximum length of a Principal Investigator CV is two pages. Any submitted CV that is longer than 2 pages may be cut off at two pages before the proposal review process begins.
We require a CV for at least the primary Principal Investigator on the proposal. We will accept CVs from each of the Principal Investigators listed on the proposal (up to two are allowed). Each CV must be limited to two pages.

Should I add a budget breakdown in my proposal?

Please do not include budget details in your proposal. We will be providing flat funding amounts based on the cost of student tuition on a regional basis.

How much funds will I get if I am awarded?

We provide support up to $60,000 USD depending on the cost of student tuition on a regional basis.

I am not eligible for this program, how can I apply to other programs?

Our website is consistently updated with new programs we offer. We encourage you to connect with our Google researchers at conferences to build more opportunities for applying to research grants.

Are Research Scholar Awards eligible for extensions?

The program is designed to support one year of work. If you are selected as a recipient of a Research Scholar award, we will partner you with a Google sponsor who can navigate the potential of an extension.

Does the co-PI need to meet the same eligibility criteria as the primary PI?

Yes, the co-PI must meet the same eligibility criteria as the primary PI. We are providing an exception if the co-PI is a postdoctoral researcher.

Can I speak to someone from the Research Scholar team to ask additional questions?

We will be providing limited email support via [email protected] . Due to the volume of emails we receive, we may not be able to respond to questions where the answer is available on the website.

Open advice to Google Research Awards proposal writers

As a part of the group of engineers that review proposals for this program, we read a lot of proposals. We'd like to read more good proposals. Here's some advice on how you can improve the content of your short proposal and make reviewing it easier.

A good research grant proposal:

Clearly specifies a problem. Good research is driven by a great problem or question, and a good proposal starts with a clearly specified one.
Describes a specific, credible, relevant outcome. Try to identify a specific and appropriately sized outcome, to give us a clear notion of what the research award would be enabling. What will likely come to be that might otherwise not happen? While this outcome should be a decisive step towards achieving your vision, it generally won't be adequate to completely achieve it. It often helps to describe both the minimum that is likely to be accomplished and a potential best-case. Since picking the right datasets and test cases is often important, tell us which ones you plan to use.
Crisply differentiates the proposed contribution from prior work. Please apply normal practices (citations, etc.) for documenting how your work will materially advance the state of the art. Make it clear how your work will be changing the state of the art, and not simply trying to match it.
Tells us how the research challenge(s) will be addressed. Successful research projects combine a great problem with ideas for solutions, too. We recognize that all the answers won't be known yet, but we'd like to feel that the direction has been established, and a plausible path has been identified. (Try to avoid proposals of the form "We want to look at problem X".) It's hard to have a big impact without taking risks, but please identify what the difficulties are likely to be and how you plan to mitigate them. It may help to explain how you succeeded in addressing analogous problems in other projects.
Puts the proposed work in context. Most projects we fund also have support from other sources. To help us understand the expected impact of Google support, please explain what funding you already have for this area of research and how the proposed work relates to your existing plans. Do you plan to build a capability for other research, provide a tool, reproduce a prior result, collaborate with others to try something out, follow up on a promising idea, or explore a new one? All are potentially of interest; we just want to know.
Makes the case to a non-expert. While we try to have your proposal reviewed by a Google expert in your field, it will also be read by non-experts, so please make at least the motivation and outcomes broadly accessible.

See past Research Scholar program recipients

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Corpus ID: 16427026

Proposal for Master Thesis in Software Engineering

Published 2005
Computer Science

13 References

Requirements engineering: processes and techniques, software process improvement: practical guidelines for business success, addison wesley longman, uk, 1997, 447 pp, software product lines: practices and patterns, experimentation in software engineering: an introduction, ethnographic responsibility without the “real”, ethnography, related papers.

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Continuing to Part III

Early in Michaelmas Term you need to submit a project proposal that describes what you plan to do and how you plan to evaluate it. In order to help with this process, you are assigned two Project Checkers, who, together with your Supervisor and Director of Studies, will provide advice on your ideas. The deadline for project proposals is a little over one week into term, and is a hard deadline .

Choosing a project

You have a great deal of freedom in the selection of a project, and should start narrowing down the possibilities by identifying starting points or ideas that appeal to you. These initial ideas should be refined to a coherent project plan, which is then submitted as the project proposal. The proposal will be discussed informally with your Project Checkers, but is then submitted to the Head of the Department as a formal statement of intent.

The main sources of inspiration are commonly:

Ideas proposed by candidates.
Suggestions made by Supervisors or Directors of Studies.
The project suggestions on the projects web page .
Past years’ projects. Most recent dissertations are available to read online ,
Proposals put forward by industry, especially companies who have provided vacation employment for students.

When ideas are first suggested or discussed it is good to keep an open mind about them—a topic that initially seems very interesting may prove unreasonable on further consideration, perhaps because it will be too difficult. Equally, many ideas on topics that are unfamiliar to you will need study before you can appreciate what would be involved in following them. Almost all project suggestions should also be seen as starting points rather than fully worked out proposals.

Notes on project choice

Some project ideas can be discarded very quickly as inappropriate. It is almost always best to abandon a doubtful idea early on rather than to struggle to find a slant that will allow the Project Checkers to accept it. Projects are expected to have a significant Computer Science content; for example, writing an application program or game-playing program, where the main intellectual effort relates to the area supported rather than to the computation, are not suitable. Projects must also be about the right size to fit into the time available. The implications of this will best be judged by looking at past years’ projects and by discussing plans with a Supervisor or Project Checker. They should not allow you to waste much time considering either ideas that would prove too slight or ones that are grossly overambitious.

It is important to pick a project that has an achievable core and room for extension. You should pick a suitably challenging project, where you will likely have to learn new things in order to successfully complete it. In addition, it is expected that you will make use of existing libraries and tools (i.e. don’t reinvent the wheel) unless there is a good reason for producing your own implementation.

Re-use of projects that have been attempted in the past

Projects are intended to give you a chance to display your abilities as a computer scientist. You are not required (or indeed expected) to conduct research or produce radically new results. It is thus perfectly proper to carry out a project that has been attempted before, and it is commonplace to have two students in the same year both basing their projects on the same original idea.

In such cases it is not acceptable to run a simple action replay of a previous piece of work. Fortunately all projects of the required scale provide considerable scope for different approaches; producing a new variation on an existing theme will not be hard. Furthermore the report produced at the end of a previous attempt at a project will often identify areas that led to unexpected difficulties, or opportunities for new developments—both these provide good scope for putting a fresh slant on the ideas involved.

Supervision

In some cases the most critical problem will be finding a suitable project Supervisor, somebody whom you will see regularly to report your progress and obtain guidance about project work throughout the year. This might be one of your main course Supervisors or a separate, specialist project Supervisor, but it should not be assumed that a person suggesting a project will be willing to supervise it. Supervisors have to be appointed by your Director of Studies, but in most cases it will be left up to you to identify somebody willing and able to take on the task. The Project Checkers will be interested only in seeing that someone competent has agreed to supervise the project, and that your Director of Studies is content with that arrangement.

Each project will have a number of critical resources associated with its completion. If even one of these fails to materialise then it will not be possible to proceed with a project based on the idea; your Director of Studies can help you judge what might be a limiting issue.

The project proposal must contain as its last section a Resources Declaration. This must explicitly list the resources needed and give contact details for any person (apart from yourself) responsible for ensuring their availability. In particular, you should name the person responsible for you if your work requires access to the Department research area. The signatures of these people should also be present on the project cover sheet before submission.

What qualifies as a critical resource?

In some cases a project may need to use data or build on algorithms described in a technical report or other document known to exist but not immediately available in Cambridge. In this case, this must be considered critical even if work could start without the report or data.

Using any hardware or software other than that available through a normal student account on UIS equipment (e.g. MCS) is considered non-standard. This includes personal machines, other workstations (e.g. research machines in the Department), FPGA boards, or even Raspberry Pis if they belong to someone else. Likewise, use of software written or owned by someone else that is not freely available as open-source will be considered as non-standard and should be declared.

Additional MCS Resources

It is reasonable to suppose that disk space and machine time will be made available in amounts adequate for all but extreme projects. Those who consider they may need more should provide a reasoned estimate of the resources required in the project proposal in consultation with the Supervisor. Additional file space should be requested through a web form , noting that:

you should state in your application that you are Part II CST;
requests for small increases of MCS space will need a very brief justification: please don't send your proposal;
requests for substantial increases should also be accompanied by a brief supporting email to [email protected] from your Supervisor.

Note that some MATLAB toolkits are not available on the MCS but might be available on Department accounts.

Use of your own computer

If you are using your own computer, please state its specifications and also state your contingency plan in case it should fail (such as using MCS or another personal computer). Please also state your file backup plan and the revision control system you plan to use. If using your own computer please include the following text in your declaration:

I accept full responsibility for this machine and I have made contingency plans to protect myself against hardware and/or software failure.

Department Accounts

Access to Departmental computers can be granted if there is a good reason, e.g.

collaboration with a particular research group;
use of software not available on the MCS facility.

If you plan to use a Department account then state this and explain why it is needed in your resources declaration. If relevant, the signature of a sponsoring member of the department (e.g. the owner of the specific resource) is required as an extra signature on the project cover sheet. In addition, your Supervisor should send an email to [email protected] requesting the account with a brief justification.

Some Department resources and the people who can authorise their use:

Requests for resources involving a Department research machine should be authorised by a Lecturer, Reader or Professor who is in charge of managing the equipment.

Access to the Department can be granted if there is a good reason. If you require access to the secure part of the William Gates Building, you should state who will be responsible for you whilst you are on the premises. They should sign your Project Proposal Coversheet as a Special Resource Sponsor.

Third-Party Resources

Resources provided by your College, other University departments or industrial collaborators must be declared. The name and contact details (including email address) of the person in charge of the resource must be stated and their signature must be present on the project cover sheet. Resources from third parties can sometimes disappear unexpectedly, so please state why you believe this is not going to happen or else state your contingency plan in case it does.

In the case of projects that rely on support from outside the University it will be necessary to procure a letter from the sponsors that confirms both that their equipment will remain available right up to the end of the academic year and that they understand that the results of work done by students cannot be viewed as secret or proprietary.

You should bear in mind that the Examiners will require electronic submission of your dissertation and code. Therefore, you should not sign anything, such as a non-disclosure agreement, that would prevent you from submitting them.

Working with human participants

If your project involves collection of data via surveys, interviews or online, release of instrumented software, fieldwork, or experiments with human participants, such as usability trials or asking people to evaluate some aspect of your work, then you must seek approval by submitting a human participants request to the departmental Ethics Committee and record that you are going to do this, by ticking the appropriate box on your cover sheet. This must occur before any of these activities start. Please read the Department's ethics policy .

Your project Supervisor will help you to fill in an online form ( read-only version ) containing two questions:

A brief description of the study you plan to do;
The precautions you will take to avoid any risk.

Simple guidance related to the most common types of study is available on the School of Technology Research Guidance site . You may also find it useful to discuss your plans with the person supervising you for the Part II HCI course.

After submitting the ethics review form, you will receive feedback from the Ethics Committee within a few days. You must not start any study involving human participants without approval from the Ethics Committee.

Planning the project

As part of the project proposal, you should provide a detailed description of the work that needs to be performed, broken down into manageable chunks. You will need to identify the key components that will go to make up your final product. Credit is awarded specifically for showing a professional approach using any relevant management or software engineering methods at all stages of project design, development and testing. Plan an order in which you intend to implement the project components, arranging that both the list of tasks and the implementation order provide you with a sequence of points in the project where you can assess progress. Without a set of milestones it is difficult to pace your work so that the project as a whole gets completed on time.

When you have decomposed your entire project into sub-tasks you can try to identify which of these sub-tasks are going to be hard and which easy, and hence estimate the relative amounts of effort involved in each. These estimates, together with the known date when the dissertation must be submitted, should allow you to prepare a rough timetable for the work. The timetable should clearly make allowance for lecture loads, unit-of-assessment coursework, vacations, revision and writing your dissertation. Looking at the details of such a plan can give you insight into the feasibility of the project. Ideally you should plan to start writing the dissertation at least six weeks before the submission date.

Languages and tools

It will also be necessary to make decisions about operating systems, programming languages, tools and libraries. In many cases there will be nothing to decide, in that the essence of the project forces issues. However, where you do have a choice, then take care to balance out the pros and cons of each option. It is expected that students will be prepared to learn a new language or operating system if that is a natural consequence of the project they select.

Uncommon languages or ones where the implementation is of unknown reliability are not ruled out, but must be treated with care and (if at all possible) fall-back arrangements must be made in case insuperable problems are encountered.

Risk management

Projects are planned at the start of the year, and consequently it can be hard to predict the results of decisions that are made; thus any project proposal involves a degree of risk. Controlling and managing that risk is one of the skills involved in bringing a project to a successful conclusion. It is clear where to start: you should identify the main problem areas early and either allow extra margins of time for coping with them or plan the project so that there are alternative ways of solving key problems. A good example of this latter approach arises if a complete project requires a solution to a sub-problem X and a good solution to X would involve some complicated coding. Then a fall-back position where the project can be completed using a naive (possibly seriously inefficient, but nevertheless workable) solution to X can guard against the risk of you being unable to complete and debug the complicated code within the time limits.

Planning the write-up

As well as balancing your risks, you should also try to plan your work so that writing it up will be easy and will lead to a dissertation in which you can display breadth as well as depth in your understanding. This often goes hand-in-hand with a project structure which is clearly split into sub-tasks, which is, of course, also what you wanted in order that your management of your work on the project could be effective.

A good dissertation will be built around a varied portfolio of code samples, example output, tables of results and other evidence of the project’s successful completion. Planning this evidence right from the start and adjusting the project specification to make documenting it easier can save you a lot of agony later on.

Preparing the Project Proposal and consulting Project Checkers

You should keep in touch with both your Project Checkers from the briefing session until the final draft of your project proposal, making sure that they know what state your planning is in and that they have had a chance to read and comment on your ideas. Project Checkers will generally be reluctant to turn down a project outright, but if you feel that yours sound particularly luke-warm about some particular idea or aspect of what you propose you would do well to think hard (and discuss the issues with your Supervisor) before proceeding. If Project Checkers declare a project plan to be unacceptable, or suggest that they will only accept subject to certain conditions, rapid rearrangement of plans may be called for.

Dealings with your Project Checkers divide into three phases between the briefing session and submitting your proposal. Most of the communications will be best arranged by Moodle comments in the feedback box and all submissions of work are on Moodle. Please be sure to take note of the various deadlines .

Phase 1 report: Selecting a topic

You start by preparing a Phase 1 report which, for 23/24 must be submitted on or before the first day of Michaelmas Full Term in October Please pay careful attention to the points raised in the briefing lectures regarding selection of an appropriate topic. You must certainly choose something that has a defined and achievable success criterion. Note also that the marking scheme explicitly mentions preparation and evaluation, so please select something that will require a corresponding initial research/study phase and a corresponding (preferably systematic) evaluation phase.

You should complete a copy of the “Phase 1 Project Selection Status Report” and upload it to Moodle .

Phase 2: Full proposal draft: Filling out details

The details will include:

Writing a description, running to a few hundred words.
Devising a timetable, dividing the project into about 10 work packages each taking about a fortnight of your effort. The first couple of these might be preparatory work and the last three writing your dissertation, with the practical work in the middle. These should be identifiable deliverables and deadlines leading to submission of your dissertation at the beginning of the Easter Term. You will probably write your progress report as part of the fifth work package.
Determining special resources and checking their availability.
Securing the services of a suitable Supervisor.

Send all this to your Project Checkers and ask them to check the details.

Phase 3: Final proposal

In the light of your Project Checkers’ comments, produce a final copy in PDF format.

You do not secure signatures from your Project Checkers at this stage. Simply submit the proposal.

Shortly after submission the Project Checkers will check your proposal again and, assuming that the foregoing steps have been followed carefully, all should be well and they will sign the proposal to signify formal acceptance. If the proposal is not acceptable you will be summoned for an interview.

Submission and Content of the Project Proposal

Completed project proposals must be submitted via Moodle by noon on the relevant day.

Format of the proposal

A project proposal is expected to up to 1000 words long. It consists of the following:

A standard cover sheet
The body of the proposal (see below).

When emailing drafts of your proposal to Project Checkers, please make sure they contain all of the information required on the final cover sheet.

The body of the proposal should incorporate:

An introduction and description of the work to be undertaken.
A statement of the starting point.
Description of the substance and structure of the project: key concepts, major work items, their relations and relative importance, data structures and algorithms.
A criterion that can later be used to determine whether the project has been a success.
Plan of work, specifying a timetable and milestones.
Resource declaration.

Introduction and description

This text will expand on the title quoted for your project by giving further explanation both of the background to the work you propose to do and of the objectives you expect to achieve. Quite often a project title will do little more than identify a broad area within which you will work: the accompanying description must elaborate on this, giving details of specific goals to be achieved and precise characterisations of the methods that will be used in the process. You should identify the main sub-tasks that make up your complete project and outline the algorithms or techniques to be adopted in completing them. A project description should give criteria that can be used at the end of the year to test whether you have achieved your goals, and should back this up by explaining what form of evidence to this effect you expect to be able to include in your dissertation.

Starting point

A statement of the starting point must be present to ensure that all candidates are judged on the same basis. It should record any significant bodies of code or other material that will form a basis for your project and which exist at project proposal time. Provided a proper declaration is made here, it is in order to build your final project on work you started perhaps even a year earlier, or to create parts of your programs by modifying existing ones written by somebody else. Clearly the larger the input to your project from such sources the more precise and detailed you will have to be in reporting just what baseline you will be starting from. The Examiners will want this section to be such that they can judge all candidates on the basis of that part of work done between project proposal time and the time when dissertations are submitted. The starting point should describe the state of existing software at the point you write your proposal (so work that you may have performed over the summer vacation is counted as preparatory work).

Success criterion

Similarly, a proposal must specify what it means for the project to be a success. It is unacceptable to say “I’ll just keep writing code in this general area and what I deliver is what you get”. It is advisable to choose a reasonably modest, but verifiable, success criterion which you are as certain as possible can be met; this means that your dissertation can claim your project not only satisfies the success criterion but potentially exceeds it. Projects that do not satisfy the success criterion are, as in real life, liable to be seen as failures to some extent.

You will need to describe how your project is split up into two- or three-week chunks of work and milestones, as explained in the planning section .

Resource declaration

You should list resources required, as described in the resources section .

Failure to submit a project proposal on time

Any student who fails to submit a project proposal on time is in breach of a Regulation and will no longer be regarded as a Candidate for Part II of the Computer Science Tripos. The Chairman of Examiners will write to the appropriate Senior Tutor as follows:

Dear Senior Tutor,

XXX has failed to submit a project proposal for Part II of the Computer Science Tripos. The Head of Department was therefore unable to approve the title by the deadline specified in Regulation 17 for the Computer Science Tripos [Ordinances 2005, p268,amended by Notices (Reporter, 2010-11, pp.94 and 352, http://www.admin.cam.ac.uk/univ/so/2011/chapter04-section9.html#heading2-43 )]. XXX is therefore in breach of the regulation and is thus no longer eligible to be a Candidate for Part II of the Computer Science Tripos. Please could you take appropriate action. I am copying this letter to the Secretary of the Applications Committee of the Council.

Yours sincerely,

------------------------- Chair of the Examiners Department of Computer Science and Technology William Gates Building JJ Thomson Avenue Cambridge, CB3 0FD

Department of Computer Science and Technology University of Cambridge William Gates Building 15 JJ Thomson Avenue Cambridge CB3 0FD

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Examples of research proposals

How to write your research proposal, with examples of good proposals.

Research proposals

Your research proposal is a key part of your application. It tells us about the question you want to answer through your research. It is a chance for you to show your knowledge of the subject area and tell us about the methods you want to use.

We use your research proposal to match you with a supervisor or team of supervisors.

In your proposal, please tell us if you have an interest in the work of a specific academic at York St John. You can get in touch with this academic to discuss your proposal. You can also speak to one of our Research Leads. There is a list of our Research Leads on the Apply page.

When you write your proposal you need to:

Highlight how it is original or significant
Explain how it will develop or challenge current knowledge of your subject
Identify the importance of your research
Show why you are the right person to do this research
Research Proposal Example 1 (DOC, 49kB)
Research Proposal Example 2 (DOC, 0.9MB)
Research Proposal Example 3 (DOC, 55.5kB)
Research Proposal Example 4 (DOC, 49.5kB)

Subject specific guidance

Writing a Humanities PhD Proposal (PDF, 0.1MB)
Writing a Creative Writing PhD Proposal (PDF, 0.1MB)
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Princeton Research Computing

2024 Research Software Engineer Call For Proposals

Since 2016, the Princeton Institute for Computational Science & Engineering (PICSciE) and Research Computing have partnered with multiple academic departments to assemble a team of Research Software Engineers (RSEs) who provide dedicated software development expertise and services to Princeton-led research projects. For more information about the current RSE program structure and organization, please refer to the RSE Partnership Guide . Some recent RSE projects are shown on the RSE Selected Project Page . The Offices of the Provost and the Dean for Research are providing substantial new resources to the existing program. To identify areas where these positions are most needed, we are using a two-step application process:

A letter of intent, (due March 1) followed by
A 3-page proposal (due May 1).

We invite Principal Investigators (PIs) from any division to submit a letter of intent, as a plain text abstract via https://easychair.org/conferences/?conf=rse24 , no later than Friday, March 1, 2024.

The call is open to all those in roles that grant PI-status as shown in the Principal Investigator Qualification Chart by Rank from the University Research Board.

The 1-page letter should explain how your team would benefit from increased expertise in research software engineering. Following an initial screening process, Curt Hillegas and Ian Cosden will hold “town hall” meetings to discuss the next step, namely, the preparation of a full proposal, which will be due on Wednesday, May 1, 2024.

Full proposals, no more than 3 pages in length, should describe the following:

The research area
How a professional RSE can enhance the research
The expertise that would be expected of such a person
The expected amount of resources needed (e.g., 50% of an RSE for 3 years)
Information about any matching funding that may be provided (e.g., 50% co-funding)

Note : Co-funding is not required but is encouraged where possible.

Proposals will be evaluated by the RSE Program Steering Committee .

For additional information, please contact Ian Cosden at [email protected] .

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State of Proposals 2024

Distilling the data to reveal our top tips for doing more business by upping your proposal game.

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How to Write a Software Proposal (With Examples)

The software—or SaaS (software as a service) —industry provides customers with a unique mix of both products and services.

Many software companies offer implementation services, custom integration projects, employee training, and customer success manager check-ins. Large enterprise and government organizations tend to need many of these services in addition to the software subscription in order to help them get results from the platform.

For those reasons, software proposals are commonplace. Large companies are more likely to purchase software from a proposal than a one-size-fits-all subscription on the company’s website. A software proposal can clarify what an engagement includes while also selling a prospective customer on the fact that they will be fully supported in their quest for ROI.

What’s in this guide:

What is a software proposal?

Software proposal examples, how to prepare for writing a software proposal, how to write a software proposal, 5 software proposal writing tips.

Enjoy proposal templates, e-signatures, follow-ups, and analytics with Proposify.

A software proposal is a document sent to a prospective client to present the value of a software product and clarify supporting services included with the contract, such as training and onboarding. Proposals sent with proposal software can include the terms of service and e-signatures so that—once accepted—the proposal acts as a binding contract between two parties.

The ideal format for a software proposal includes an overview of the client’s goals, the solution, and what sets the company apart.

While one software proposal will showcase only the features and customer support as in the solution section, another might include very detailed project phases for implementation and training.

What are the types of software proposals?

The main types of software proposals are enterprise software proposals and SaaS proposals .

Enterprise software proposals - Enterprise software caters to large companies and organizations. Implementation is far more complex, so these types of proposals tend to include not only details about the proposed solution but also rollout timelines and milestones and the scope of work for data integration or data onboarding.

SaaS proposals - SaaS proposals, on the other hand, often only sell software access. There won’t be custom project deliverables. The SaaS proposal might include some details about customer support access, but there won’t be a ton of project management involved. These proposals are aimed at startups, solopreneurs, and small businesses.

Keep in mind that a single company can send both types of proposals to cater to their different customer segments. Many successful companies have SaaS and enterprise branches.

Software development proposals are completely different. These software project proposals are for custom web app or mobile app development . The client will pay for the development process. Meanwhile, a software proposal (described in this guide) is designed for selling access to software that already exists and whatever custom services will help a client make the most of it.

A great proposal should be detailed, brief, and results-focused.

Here are a couple of examples you can use as inspiration.

Enterprise software proposal example

There are several things you can learn from this enterprise software proposal .

For starters, it includes an Overview and Goals page that mentions the client’s business growth, and associated challenges. The page then offers a bulleted list of the solutions needed to address those issues.

Enterprise software proposal example showing overview and goals page

This proposal also includes the project scope. The work is broken down into three main categories: enterprise application management, customer relationship management, and business intelligence. On the second page of the Scope of Services section, the proposal lists the software products included and gives an overview of the project timeline.

Enterprise software proposal example showing project scope page

Overall, the proposal serves as a great example of how to combine software access and hands-on work.

View the entire proposal example and use it as a template.

SaaS proposal example

A SaaS proposal is usually a lot simpler, because there’s typically less custom work involved.

This SaaS example offers a few lessons. The Why Us section includes the company’s beliefs, history, value proposition, and commitment to customer support in just a few short paragraphs. Use this as inspiration when crafting your own Why us page.

SaaS software proposal example showing 'Why Us?' page

The What We Offer portion of the proposal does an excellent job of combining software features and business benefits together. Make sure to use this section of the proposal to spell out the results of using your software, not just the functionality.

SaaS software proposal example showing 'What We Offer' page

This SaaS proposal example also includes a bright, on-brand pricing section with different options. Clients can select the best fit for their business, and then sign off on the subscription.

SaaS software proposal example showing pricing page

All in all, the proposal does an excellent job of selling the software in the company’s unique brand voice and distilling essential details into a single document.

Before you can write a proposal, you need first to understand the client’s business case for your software.

How to identify the clients’ needs

Asking the right questions is the most important part of any sales process. Client discovery always comes before writing a proposal.

Client discovery can be done in a single software demo or a series of sales calls, demos, and presentations with different stakeholders.

Here are some questions you might ask:

Why did you decide to book a demo of our software?

What is your team struggling with when it comes to [task or project]?

What are you currently using to manage [task or project]?

How does your team collaborate on [task or project]?

When demoing your software, pay attention to what features and customer stories they respond to the most. This can help you identify the client’s problems and goals.

What to include in the proposal

Before you sit down to write your proposal, it’s important to know what it should include so you can prepare ahead of time and gather everything you need.

The ideal software proposal outline will cover all of these sections (although the names of the sections can change):

Cover page - This entails a picture, your company’s name, the client’s name, and the date

Overview of problem and goals - Also known as an executive summary , this section serves as high-level overview of the client’s current situation, their goals, and how you can help.

Why us - This page is better than an about page because it includes company bio details as well as differentiators and unique selling propositions .

Solution - In this section, you’ll go over the software’s features and how this will directly impact the company’s goals. You can also include onboarding and support information here.

Implementation - For enterprise software, you might want a dedicated section for software implementation, where you’ll outline a custom onboarding and training project.

Case studies - Include mini case studies with customer testimonials to help the client visualize their success with your platform.

Pricing - Showcase the price and what it includes or create a table with different pricing options.

Contract and signature - To turn your proposal into a contract, make sure to include your terms of conditions and e-signatures for both your and the account point of contact.

To write a detailed proposal, follow these simple steps.

Step 1. Begin with a software proposal template

Kick things off with a proposal template to save time.

Proposify offers a variety of proposal templates for the software industry .

Using a template not only saves time with writing but with designing as well. You can send the template to your in-house graphic designer and ask them to make it on-brand, or spend an hour or so tweaking it yourself to match your company’s branding.

Step 2. Craft the re-usable sections to create your company’s own template

The next step is to craft messaging that you can reuse for different clients.

You can start with your Why Us section. It should be informed by competitive research . Let’s take PropertyRadar as an example. This real estate intelligence software offers 250+ criteria for building lists of the perfect properties (for direct mail campaigns). Most real estate analysis tools competing with PropertyRadar offer less than 50. So their salesperson might write something like, “PropertyRadar offers 5X the amount of targeting criteria so you can get better ROI on every direct mail campaign.”

All in all, you can plan on creating reusable content for these proposal sections:

Implementation

Case studies

Contract and signature

Step 3. Determine the best offer for a specific client

Before you can customize your company’s proposal template for a specific client, you need to lean on your client discovery process, your experience, and your intuition to make decisions about what to present.

When choosing the best approach for each client, here are some things to consider:

Which software subscription to steer them towards

Which add-on software products to offer

Which add-on services to offer (custom onboarding, implementation, integration, training, API development, etc.)

Whether or not to only include your desired options in the proposal or to offer multiple plans and add-ons and allow them to choose

Step 4. Get the client’s verbal agreement

Before you start customizing your proposal for a client, you might want to check that you’re all on the same page. You could host a final call to propose your software and the pricing options quickly, and see if that fits their needs and expectations. Once you’ve got the green light, go ahead and move onto the next step.

If it’s a large corporation or government agency, you could ask your point of contact what essential information the decision-makers will want to see. Even if there isn’t a request for proposal (RFP) being sent out for software solutions like yours, you could ask to see a previous, unrelated RFP so you know what this entity looks for in the proposal format.

Step 5. Revise the proposal sections that should be unique to that client

Now it’s time to customize your company’s proposal template for a specific client. Take the proposal template and alter all of these sections to match the client’s company name and their needs.

Overview of problem and goals

To make things more human and personal, you might even add photos and contact information of the specific team members who will assist them after they sign (such as onboarding specialists or customer success managers).

Step 6. Send the proposal

Now it’s time to send the proposal. Proposals with e-signatures assigned to both the sender and recipient are 426% more likely to close .

Statistics showing that proposals with e-signatures assigned to both the sender and recipient are 426% more likely to close.

To increase your chances of getting the deal closed by a further 36.8%, be sure to sign the proposal before you send it.

Step 7. Make adjustments if necessary

And finally, be prepared to make adjustments. When we researched thousands of SaaS proposals , we found that proposals that close are viewed 2.5 times on average, while unsuccessful proposals are viewed 3 times.

Statistics showing that proposals that close are viewed 2.5 times on average, while unsuccessful proposals are viewed 3 times.

When using Proposify, you can use viewing metrics to your advantage. If you notice that a prospect has viewed a proposal 3 or more times but hasn’t signed it yet, go ahead and ask them if they’d like to change the scope or explore a different solution.

To rock your software proposals, try these smart tips.

1. Include images and videos alongside text

Multimedia content can help seal the deal. Including images in a SaaS proposal boosts the closing rate by 23% , while including at least one video will bump up the closing rate by 41%.

For images, you can include team photos, software screenshots, and case study result graphs. For videos, try making a simple, friendly high-level overview of the proposal or uploading an explainer video . With Proposify, you can easily arrange images and videos on any proposal page.

Software proposal example showing videos included within the proposal.

2. Write out pricing options in interactive fee tables

When you offer interactive fee tables—a table where the client can select add-ons and quantities—you can boost closing rates by 173%.

Software proposal example showing an interactive pricing table.

This is a no-brainer. Software pricing should be customizable, especially for larger companies.

3. Try multiple forms of social proof

Unlike agencies and small firms, software companies typically garner a lot of online reviews. Websites like G2 and Capterra are well-known review sites for vetting business software.

If you’ve got a great star average or any awards from these sites, be sure to include those badges. That can help add quantity to your social proof, whereas the testimonials and case studies are more about quality .

4. Write different templates for different levels of service

As mentioned above, we recommend starting with a general software template and then customizing it to make a template that is specific to your business and can be used again and again.

You can take this strategy a step further by crafting different templates for different software products, subscriptions, or levels of service. This will drastically improve your speed when sending proposals to clients, as you’ll have to do less customization each time.

5. Benchmark your average viewing rates and closing rates to inform template revisions

To make sure that your proposal writing is on point, we recommend tracking your benchmarks. Inside of Proposify, you can check your analytics for average viewing rates and closing rates. You can then compare individual clients and proposals against these metrics.

Software proposal example showing proposal metrics and insights within Proposify app.

You might find that one proposal type closes at a better rate than another. For example, maybe your hands-off software access closes at 3X the rate as your custom implementation offer. This could inform you that your custom service is priced too high, and maybe you need to try writing up a mid-level offer with some barebones hands-on service at a lower rate.

Get even more data-driven tips in the State of Proposals.

Next steps: create a software proposal in minutes

To create and send an excellent software proposal, you need templates, e-signatures, dynamic fee tables, and analytics all in one place.

With Proposify, you get these and more features designed to boost closing rates.

Get a free Proposify demo today and see how it's done.

Dayana Mayfield is a B2B SaaS copywriter who believes in the power of content marketing and a good smoothie. She lives in Northern California. Connect with her on LinkedIn here: linkedin.com/in/dayanamayfield/

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Engineering Proposals: Free Template + 12 Proposal Writing Tips

Digital Asset Management
Marketing Technology

Posted by: Cinthya Soto

An engineering service proposal is a standardized document pivotal in guiding the selection of consultants. The engineering proposal is more than just a document; it’s your opportunity to showcase expertise, understanding, and value among the piles of proposals.

Whether you’re a seasoned engineering consultant or a firm stepping into the arena of important projects, your ability to craft a compelling proposal can make all the difference in placing your business ahead of the competition. After all, in 2023 alone companies sourced 39% of their revenue from RFPs .

However, mastering the creation of such a proposal is a complex task. For those wanting to successfully navigate this process, you must be ready to grasp the fundamental concepts of engineering proposals. But what exactly does that include?

In this blog, we’re covering everything there is to know about writing engineering proposals. From what to include in your engineering proposal and writing tips to engineering project proposal examples and a free engineering proposal template, we aim to help you create winning proposals .

How to Write an Engineering Proposal: What to Include

Engineering proposals serve as the critical document on which selections of engineering consultants are based. For consulting firms in the engineering sector, these proposals are the principal tool for winning new contracts. For clients or project owners, they are an invaluable resource to help them choose the best consultants. But what should be included in an engineering proposal format?

Here are the different sections you should include in your engineering proposal:

Cover Page

The cover page of your engineering proposal sets the first impression. It should include the project title, the name of the organization or individual presenting the proposal, the date, and any relevant project identification details. Make sure to keep it professional and clean to reflect the seriousness of your intentions.

Cover Letter

The cover letter personalizes your engineering proposal. It should briefly introduce your organization, express your enthusiasm for the project, and highlight the key points that make your proposal stand out. This is your opportunity to establish a connection with the reader and encourage them to read further.

Introduction

The proposal introduction serves as the executive summary of your proposal. It should include an overview of your organization, the purpose of the proposal, and a summary of what the proposal will cover.

Make sure to clearly define the problem or opportunity your proposal addresses. Essentially, the introduction clarifies the purpose of crafting the proposal. It should lay out the foundation for why the proposal is necessary.

Additionally, the introduction should provide a concise overview of the proposal’s content. This summary needs to be engaging and offer a glimpse of the detailed descriptions to follow. It reveals your core idea and outlines the strategy you intend to use in delivering your services. Here’s an example of what a successful proposal introduction looks like:

Project Background

This section delves into the details of the project. Describe the current situation, the specific problem or opportunity, and the objectives of the project. Providing a concise but thorough background helps the reader understand the context and the necessity of the proposed work.

You should illustrate the issue or situation that led to the creation of your proposal. In this section, it’s important to show a thorough comprehension of the problem at hand.

Qualifications

In this section, highlight your qualifications and experience relevant to the project. Detail your technical expertise, prior successful projects, and any unique skills or resources that make you stand out. This section reassures the client that you are capable of handling the project.

When writing this section, make sure to accurately highlight your skill set to emphasize your suitability for the project at hand. Consider this section as the part of your resume where you detail your skills and experiences.

While you should showcase your expertise, It’s also important to showcase, if relevant, your company’s proficiency and ability to successfully carry out the proposed task.

Project Team

Typically, an engineering proposal requires including details about each team member, their specific roles, and their professional backgrounds. The collective expertise of the project team often plays a critical role in the selection of a qualified engineering consultant, which is why the “Project Team” section is extremely important.

In this section, showcase the team that will work on the project. Ensure that every team member’s relevant experience is detailed in the proposal. Make sure to include brief employee bios in your RFP resumes that highlight each member’s qualifications and relevant experience. Just a few well-crafted sentences on such skills can make a significant difference.

Proposal example of "our team & how we work"

The right tools can help in creating and formatting these bios to present a professional image. With a robust digital asset management (DAM) solution like OpenAsset , you can cut down on the hours spent creating resumes and employee bios for engineering proposals.

A notable feature for employee bios is Content Assist , powered by OpenAsset’s Generative AI. It’s designed to assist users in creating project descriptions and employee bios within OpenAsset. In simple terms, Content Assist will analyze the existing data in your OpenAsset system to create original project descriptions and employee bios.

OpenAsset and the Employee Module enhance productivity within your organization by automating the creation of employee resume documents . This helps you present your team in the best light possible and quickest way possible.

Remember, the strength of your team and how you present your team can be a significant deciding factor in winning the proposal.

Scope of Work

In the scope of work , you’ll want to detail the specific activities, deliverables, and timelines involved in the project. It’s crucial for an engineering proposal to precisely outline the project scope. Given that misunderstandings about project scope are a primary cause of project issues or failures, defining the scope with as much clarity is essential to prevent and more easily resolve problems.

Clearly outline the work that will be done, the methodologies used, and the expected outcomes. This section should align expectations and minimize misunderstandings about the project’s scope. When the scope is clearly defined, everyone involved understands the extent of work expected within the project’s framework.

Work Schedule

The work schedule section of the proposal does precisely what its name suggests: it monitors your work schedule.

Provide a detailed schedule of the project, including key milestones, deadlines, and dependencies. This section maps out the timeline for completing the project and is crucial as it informs your audience about the expected timelines and milestones.

The work schedule should be realistic and allow for some flexibility. You can enhance clarity by incorporating well-organized tables and specific time allocations. It’s also crucial to demonstrate your understanding of project management and your ability to complete the project on time.

Including a work schedule provides a professional touch to your proposal. Should you find yourself unable to stick to this schedule once the project has begun, it’s crucial to communicate any changes as soon as possible with the relevant parties.

Project Cost

Undoubtedly, the cost of engineering services holds significant weight in the decision-making process. The potential consultant is expected to define costs for each team member based on suitable hourly rates and the estimated hours needed for each stage of the project, along with a summary. While engineering associations recommend choosing consultants based on qualifications above cost, the reality is that pricing remains a key determinant.

Project fees and schedule example in proposal

In this section, detail the expected expenses for your project by itemizing them and assigning monetary values to each category. Break down the costs into distinct groups, such as individual labor charges per employee, material costs, and so on. Summing up these figures will provide the total cost, offering the reader a clear financial overview.

This facilitates an informed decision-making process, allowing for a thorough assessment of different factors before committing funds.

Write a Strong Proposal Conclusion

The conclusion of the proposal mirrors the concluding section of a cover letter. Here, you should explain the reasons for considering you or your team as the ideal choice for the project and provide your contact information. It’s also an opportunity to reaffirm why you or your team is the most suitable candidate for the project.

Conclude your proposal by summarizing the main points, reiterating the benefits of choosing your organization, and expressing your eagerness to work on the project. End on a positive note, inviting further discussion or questions. You want to write a compelling conclusion that will leave a lasting impression on the reader.

now.

Expert Engineering Project Proposal Tips

To maximize your chances of winning projects with your engineering proposals, here are 12 proposal writing tips to keep in mind when crafting your next engineering proposal:

1. Read the RFP Multiple Times

Simply reading the Request for Proposal (RFP) is not enough; a deep understanding of the evaluation criteria is crucial. It’s essential to review the RFP carefully, taking in every detail beyond the basic requirements such as submission dates, formatting guidelines, and required signatures.

This thorough examination ensures you are aligned with all the requirements, conditions, and expectations outlined in the RFP. Such attentiveness not only aids in crafting a tailored response that aligns with the issuer’s needs but also minimizes the risk of non-compliance with the RFP’s requirements.

Moreover, upon reading a new RFP, it’s smart to draft a checklist detailing all essential criteria. This approach allows a good amount of time for you to make sure everything is included in your RFP response and allows you to request any necessary clarifications well in advance of the deadline.

2. Do Your Research

Undertaking comprehensive research is a cornerstone in the development of a compelling engineering proposal. This process involves gathering data, understanding industry standards, exploring the project’s context, and analyzing the potential impact of your work.

Research allows you to approach the project with a depth of knowledge that will reflect the feasibility, innovation, and planning of your proposal. It also provides a foundation for making informed decisions, identifying potential risks, and proposing effective solutions.

In essence, the research you conduct forms the backbone of a proposal that’s both convincing and achievable.

3. Create a Clear Proposal Format

If you’re wondering how to draft a proposal, starting with a clear format is a great start. Maintaining a clear engineering proposal format is fundamental to creating a structured and professional document. A standardized format ensures that the information is presented in an organized manner, making it easy for the reviewers to follow and evaluate. The format typically includes a well-defined table of contents, sections with clear headings, and a logical flow of information.

This structured approach helps display your message with precision and prevents critical elements from being overlooked. Consistency in format across various sections, such as the project background, scope, schedule, and qualifications, reinforces the overall flow of the proposal.

A well-formatted proposal not only reflects your professionalism but also helps in communicating the seriousness and preparedness of your team for the project.

4. Introduce Your Company’s Background

Introducing your company with a concise background in your engineering proposal is a critical step in setting the stage for a strong pitch. This section should provide a snapshot of your company’s history, core competencies, successes, and mission as they relate to the project in question. It’s an opportunity to establish credibility and build trust with the proposal’s reviewers.

This introduction should show the essence of your company’s identity, values, and the journey that has led to its current standing in the industry. Highlighting notable achievements, experience in similar projects, and the overarching vision can create a compelling narrative that resonates with the potential client.

A well-articulated company background serves as the foundation upon which the rest of the proposal is built, underlining why your firm is uniquely suited for the project.

5. Feature Your Team’s Talent

In an engineering proposal, it’s important to select the best employees for your RFP response . Make sure to illustrate the unique skill sets, qualifications, and experiences that each member brings to the table. You can use this section to delve into the specific talents that differentiate your team from competitors.

Detailing individual roles and how they will contribute to the success of the project not only showcases the depth of your collective proficiency but also helps build confidence in your team’s ability to deliver results. Remember to align these talents with the project requirements, demonstrating a perfect fit between your team’s capabilities and the project’s needs.

If you’re looking to generate employee resumes in minutes that demonstrate your team’s talent, a robust DAM can help you store and manage employee profiles. A DAM for engineering like OpenAsset saves you time and resources in managing your resumes. Through the use of Generative AI, you can create project descriptions and employee RFP bios.

6. Include Images and Graphics

Incorporating images and proposal graphics into your engineering proposal can significantly enhance its impact. Civil engineering projects typically require various diagrams, illustrations, and maps. This is because the essence of designs is best conveyed through visual representation for complete clarity. The same principle applies to proposals, where visual elements are crucial for demonstrating complex information clearly and effectively.

Moreover, using images to communicate visually will save reading time for the client. The reader will also appreciate the amount of effort put into preparing the engineering proposal.

Six images listing the types of proposal graphics to include in proposals

Use tools like OpenAsset to seamlessly find and present visuals of projects that align closely with the prospective client’s needs or showcase your team’s qualifications. This tailored visual approach not only shows your past successes but also provides a compelling, concrete visual narrative of what you can deliver.

Moreover, AI in DAM saves you time and resources in managing your images through AI-suggested keywords, image similarity search, AI Content Assist, and more. These features:

Reduce the time it takes to manually tag images
Helps you build and expand taxonomies
Gives you additional images to select from and use.
Enables you to leverage Generative AI for project descriptions and employee RFP bios

Images are not just embellishments; they can be powerful testimonials of your work’s relevance and quality, speaking volumes more than words alone.

7. Use Clear Language

Using clear and concise language is crucial when crafting an engineering proposal. The ability to demonstrate complex ideas effectively without resorting to overly technical jargon or unnecessarily complicated explanations is a skill that cannot be overstated.

Likewise, it’s important not to assume that the readers will be familiar with the specialized jargon of your field. Take time to research the client and the reviewing committee to grasp their expertise and knowledge base. When delving into detailed technical matters, ensure you offer sufficient background to maintain inclusivity and prevent any misunderstandings.

Proposals should be accessible, ensuring that stakeholders, regardless of their technical background, can grasp the proposed concepts and see the value in them.

Your team might possess attributes such as “remarkable efficiency, strong motivation, and outstanding qualifications,” yet, it’s likely that your competitors claim the same. Such language is often so overused in proposals that they become clichéd and lose their impact.

Rather than relying on generic adjectives, prove your capabilities with concrete examples of past projects or situations that illustrate your skills in action. Even more compelling would be citing testimonials or endorsements from previous clients who can vouch for your expertise. Here’s what that could look like in your proposal:

Past projects and testimonials example page in proposal

Additionally, avoiding excessive language not only helps in maintaining the reader’s attention but also demonstrates your ability to communicate efficiently. The proposal should be as concise as possible without sacrificing completeness. Every sentence should serve a purpose, whether it’s to inform, persuade, or clarify.

8. Keep It Short

Coming across an RFP with a large page limit or no page limit might give you the impression that you should create a significantly lengthy proposal. However, it’s best to resist this urge to write a 200-page proposal.

Keep in mind, your potential client will be comparing your proposal with several others. A concise submission allows them to quickly discern the essential details. Overloading your proposal with text increases the likelihood of them skimming over key points — steer clear of dense paragraphs.

A concise proposal not only respects the reviewer’s time but also enhances readability and comprehension. It’s essential to spread your message and value proposition without unnecessary elaboration. Precision in language, clarity in presenting solutions, and conciseness in your descriptions can make a powerful impact.

Moreover, a proposal that is straight to the point is often a sign of a well-thought-out project plan and a capable, organized team that knows how to communicate. A compact and well-structured proposal often speaks volumes about your project management skills and your respect for the client’s resources and time constraints.

9. Practice Teamwork

A collaborative approach ensures that diverse expertise and perspectives contribute to a more robust and comprehensive document. It’s the teamwork among team members, with their unique skill sets and experiences, that can elevate the quality of a proposal.

Working together with your team in the proposal process can lead to innovative solutions that might not surface in a siloed work environment. Teamwork facilitates thorough cross-checking, brainstorming, and problem-solving, which are essential to addressing the complex challenges typically presented in engineering projects.

Remember, surrounding yourself with an effective team is the key to success. A collaborative culture could be what makes you stand out from the competition.

Moreover, highlighting the collaborative nature of your team within the proposal can also serve as a testament to your capability to work together, a quality often wanted by clients. It’s not just about the final product but also about demonstrating the process of how your team works together to achieve excellence.

10. Proofread and Double-Check

Proofreading and carefully double-checking your proposal is as essential as the content itself. This step ensures that your document is free from errors, which could otherwise affect the credibility of your proposal. It’s not only about catching typos or grammatical mistakes; it’s about ensuring that every figure, fact, and statement aligns with the RFP requirements and your research findings.

A proposal that is well-edited and error-free communicates attention to detail and a commitment to excellence. It’s recommended to have multiple team members review the proposal to provide fresh perspectives and catch issues that you might have overlooked.

Remember, a single mistake could raise doubts about the professionalism of your work and the quality of the project delivery. Therefore, rigorous proofreading and double-checking are necessary to validate the integrity and professionalism of your engineering proposal.

11. Meet Deadlines

Meeting deadlines is important in the context of engineering proposals. The ability to deliver on time reflects your professionalism and reliability. It’s a non-negotiable element of project management that sets the stage for how potential clients view your commitment to the project’s success.

Planning is essential to ensure deadlines are not just met, but comfortably, allowing planning for any unexpected circumstances that might arise. Missing a deadline can have significant repercussions, from damaging your reputation to disqualification from the bidding process.

However, it’s not just about avoiding the negative. Following deadlines can also enhance your standing with clients, as it demonstrates respect for their time and trust in your ability to manage the project effectively from the start.

12. Submit the Proposal on Time

Part of meeting deadlines includes submitting your proposal on time. Every RFP specifies a submission deadline that must be met. Contrary to project deadlines, which may be subject to change during a project, the cut-off date for proposal submission is non-negotiable.

Physical submissions of hard copy proposals are often marked with an actual time stamp upon receipt. If that timestamp shows your submission arrived even one second past the deadline, it will unfortunately be too late.

With the rise in digital submissions, the strict adherence to deadlines continues. Considering potential issues like slow upload speeds or internet disruptions, it’s wise to plan additional time when submitting proposals electronically.

Make sure it’s clear how the proposal will be submitted so there are no surprises. You should also ask for help when it’s needed, especially if you are facing roadblocks and don’t have much time left.

Moreover, building buffer time for reviews and potential revisions is a wise strategy. Proposal writing often shows that tasks tend to extend beyond anticipated time frames. Whether you run out of printing paper or a team member makes a last-minute change, you need to be prepared. Therefore, allowing yourself a generous timeline for the preparation and submission of the final documents can mitigate the risk of missing the deadline.

Meeting deadlines reflect your professionalism and reliability, which are critical factors in the selection process. It demonstrates your commitment to the project timeline and sets the stage for the timely execution of the work ahead. Engineering proposals are also an investment of time and money and if you miss the deadline, this investment is wasted.

Additionally, a punctual proposal suggests that your project management and organizational skills are well-tuned, giving potential clients confidence in your ability to deliver results within the specified timeframe.

Free Engineering Proposal Template

At OpenAsset, we want to provide you with valuable resources to help pave the way toward success. That’s why we’re providing a free engineering project proposal template to inspire your proposal writing journey.

Use OpenAsset for Your Engineering Services Proposal

Leveraging a robust DAM, like OpenAsset , in your engineering proposals is a game-changer. OpenAsset streamlines the process of incorporating high-quality images and project data that represent your team’s past achievements and expertise.

By using OpenAsset, you can create a visually impactful and content-rich proposal that stands out. It not only saves valuable time by organizing your assets efficiently but also ensures that you present a polished, professional, and tailored proposal to your potential clients.

OpenAsset’s DAM solution makes AEC proposals simpler, faster, and more successful. That’s why 99% of customers renew. Leverage OpenAsset to elevate your proposals and effectively demonstrate why your team is the ideal choice for any AEC project.

Ready to start creating engineering proposals that will set you apart?

Get OpenAsset DAM Insights

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Engineering Project Proposal

Proposal maker.

Engineering project proposals are written for a variety of reasons. There are some engineering project proposals that are made to persuade potential sponsors, funding agencies, and/or creditors. There are also some engineering project proposals that allow the plans of engineers to be considered and approved by the management of the firm where they work for or the clients who would like to get their services. With these being said, an engineering project proposal is actually like a concept proposal in a way that it reflects and presents the specific ideas of an engineer for a particular engineering project.

An engineering project proposal must be aligned with the kind of engineering project that you would like to be a part of. It should also be the result of either a request or an existing issue, concern, or problem. To guide you in developing a clear and goal-derived engineering project proposal , we have listed several downloadable examples of engineering project proposals that are used in various activities and programs.

15+ Engineering Project Proposal Examples

Engineering proposal example.

Google Docs

Size: A4, US

Engineering Project Proposal Template

Engineering Student Project Proposal Template

Apple Pages

Size: 115 KB

Engineering Project Execution Plan Template

Size: 26 KB

Engineering Project Scope Template

Size: 162 KB

Free Engineering Project Roadmap Template

Apple Keynote
Google Slides

Size: 66 KB

Civil Engineering Project Manager Resume Template

Size: 98 KB

Agricultural Engineering Project Proposal Example

Size: 44 KB

Software Engineering Project Proposal Example

Size: 133 MB

Engineering Research Project Proposal Example

Industrial and Systems Engineering Project Proposal Example

Size: 201 KB

Chemical Engineering Project Proposal Example

Size: 601 KB

Computer Engineering Project Proposal Example

Size: 551 KB

Mechanical Engineering Project Proposal Report Example

Size: 350 KB

Engineering Project Proposal Format Example

Size: 187 KB

Mechanical Engineering Project Proposal Example

Engineering Project Proposal Example

How to Impress the Target Audience of Your Engineering Project Proposal

If you think that only consulting engineers are bound to develop an engineering project proposal, then you may want to think again. There are different engineering positions that are also required or tasked to come up with an engineering project proposal may they be in their own fields of engineering expertise or in the fields of education, advanced research, and academics.You may also see project proposal outlines .

Since there are a lot of engineering project proposals that you can create, expect that there are also different types of audiences that you need consider whenever you plan to develop this document. Here are some of the ways on how you can impress the target audience of your engineering project proposal:

1. Make sure that you will properly define the entities who support the project that you are proposing. Knowing how your audience is can give you more idea on how you can properly present the engineering project proposal in a more appealing manner. You may also see short proposals .

2. You can rely on templates and references when making an engineering project proposal but make sure that the content and discussion presentation that you will come up with must be aligned with the needs, requirements, and expectations of your audience. With this, you can identify the set quality standards that you need to meet or even exceed for the engineering project proposal to be approved. You may also see non-profit proposals .

3. Know the scope and limitations of the information that you will include in the engineering project proposal based on the type of audience for your presentation. As an example, there is a big difference with the way you present engineering project proposals to external entities and the company that you work for. External entities like clients and investors need more information about your services and offers, while your company requires the proper budgeting for raw materials and engineering workforce. You may also see fundraising proposal .

4. Be aware of the background of your clients. This will help you know the information that are essential to be included in the specific engineering project proposal that you will make. In comparison to a management plan, your engineering project proposal must also be based on how you would like your target audience to perceive the potential of your recommendations for development.

Engineering Project Proposal Content

An engineering project proposal may be the result of a proposal request from clients or the management. It can also be a direct proposition from an engineer presenting potential solutions to problems or current conditions. No matter what the purpose of the proposal writing or the creation of an engineering project proposal is, you have to keep in mind that it is important for you to ensure that you will create a comprehensive, well-detailed, and organized document. Here are some necessary information that are commonly found in an engineering project proposal:

1. Have a proposal statement that can define the scope and limitations of the engineering project proposal. You have to develop a limit to what you are proposing so that project expectations can be set. Minimum requirements can also be expected to all stakeholders of the potential engineering project if this will be done. You may also see budget proposals .

2. Write an introduction for the engineering project proposal. A brief discussion of the proposal’s description can present the need for the engineering project. This part of the engineering project proposal can also help you to present the purpose not only of the project but the usage of the proposal document as well.

3. Present the situation that made you decide to write the engineering project proposal. Are you required by the management of the business? Are you presenting the proposal to a client or another organization? Are you trying to recommend or suggest an engineering project that can benefit a target community? Knowing the purpose of the engineering project proposal can make it easier for the audience to understand the need for the proposed project to be implemented. You may also see research proposal .

4. Have a representation of the methodology that you will follow should the proposal be approved. List down all the processes that the project team and other stakeholders will undergo so that your target audience can be aware of these activities.

5. Create a work schedule for the engineering team. Present the specific time frame that you would like to execute so that you can make sure that the project is time-bound. Proposed work processes that are within specific time schedules can make the engineering project proposal more impressive. You may also see proposal memo examples.

6. Have a financial plan for the engineering project that you are proposing. List down all the cost that the business or the clients need to prepare for so that the project can be implemented. Before creating the proposal, you can already ask for the budget range of entities so that you can have a guide when selecting suppliers, contractors, and workforce providers. You may also see security proposal .

7. It is highly recommended for you to write a few information about your credentials or the milestones of your company. This is a great way to present your credibility and trustworthiness. Moreover, it can reflect your ability to provide all the deliverable that can make the project be created with quality standards.

8. Be particular with the discussion of your desired results. You have to stick with the purpose of the engineering project. Moreover, you always have to be specific when discussing this part of the engineering project proposal as this is a big factor when considering the approval of your proposal. You can also come up with a conclusion that can summarize the entire engineering project proposal discussion. You may also see development project proposals .

Suggestions, Tips, and Recommendations in Making an Engineering Proposal Project

Engineers create project proposals to present the professional work that they would like to propose so that the particular needs of different stakeholders can be provided. With the range of engineering processes that have varying practices and natures of circumstances, there are different engineering project proposals that can be created. For you to come up with a concise and comprehensive engineering project proposal, listed below are some of the suggestions, tips, and recommendations that you can refer to. You may also policy proposals .

1. Maintain organization when presenting the discussion within the engineering project proposal. You have to ensure that your audience is aware of what they can expect from you especially when it comes to work processes, quality metrics, deliverable, and timetable attainability. You may also see service proposals .

2. Use proposal examples and templates as references especially those that are aligned with the exact kind of engineering proposal project that you need to make. Always remember that you should not just look into the content of the document but the proper presentation of these information as well.

3. Know the tone of the discussion that you will incorporate in the engineering project proposal. Do you want the engineering proposal project to be engaging and persuasive? Would you like it to be business-oriented? Different writing approaches can be done depending on the kind of engineering project proposal that you are bound to make. You may also see professional proposals .

Having the right mind-set and the goal to finish an outstanding engineering proposal project can help you a lot when it comes to preparing the document. Again, feel free to refer to downloadable examples, tips, and guidelines so that you can be more efficient in using your time and effort when creating an engineering proposal project.

Text prompt

Instructive
Professional

Generate a proposal for a new school recycling program

Compose a proposal for a school field trip to a science museum.

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Software Project Proposal
FREE 11+ Engineering Project Proposal Samples in PDF
FREE 11+ Engineering Project Proposal Samples in PDF
FREE 11+ Engineering Project Proposal Samples in PDF
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Software Development Proposal Template

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Project planning

COMMENTS

Top 10 Software Engineer Research Topics for 2024
Text mining can be applied in the context of software engineering data to find patterns and trends in software development processes. 4. Data Modeling. Data modeling is an important area of research paper topics in software engineering study, especially in the context of the design of databases and their management.
PDF Proposal for master thesis in software engineering
Research methodology: Our proposed methodology is quite straightforward for this study, and consists of three main steps, which are connected to the intended structure of the entire thesis. Step 1: In order to investigate and answer the first research question, a quite extensive literature review will be preformed as a first point.
(PDF) Guidelines for Conducting Software Engineering Research
Klaas-Jan Stol and Brian Fitzgerald. Abstract. This chapter presents a holistic ov erview of software engineering research. strategies. It identiﬁes the two main modes of research within the ...
PDF Project Proposal CSE 403
CSE 403 - Software Engineering. engobada), Nikhil Karkarey (nikhilk)IntroductionMost of the calendars and planner applications available today only displ. y a schedule which the user has manually planned. They do not offer any smart scheduling or schedule optimizing features to assist the user in planning their day, b.
(PDF) A review of software engineering research from a design science
1 Introduction. Design science is a paradigm for conducting and communicating applied re-. paradigm may be a viable way to presen t research contributions in existing. search contributions comm ...
Proposal for master thesis in software engineering
Download Free PDF. View PDF. 2009 •. Wasif Afzal. Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and costeffective software verification and validation activities are both a priority and a necessity considering the pressure ...
PDF Writing Good Software Engineering Research Papers
Minitutorial. Mary Shaw. Carnegie Mellon University [email protected]. Abstract. Software engineering researchers solve problems of several different kinds. To do so, they produce several different kinds of results, and they should develop appropriate evidence to validate these results. They often report their research in conference papers.
Topics and Proposals
Software deployment, configuration and maintenance. Software project management, planning and audit. Software ethics, licensing and collaboration. Software evolution, in particular, is a branch of software engineering focused on studying existing software and not necessarily creating new one. It covers, among other topics: Mining repositories ...
PDF SAMPLE RESEARCH PROPOSAL
SCOPE. Following tasks will be undertaken as a part of the proposed research-. Task 1. Task 2. Task 3, etc. METHODOLOGY AND APPROACH. This section needs to answer self-imposed questions and should reflect that the student has good understanding of the problem and of the barriers in the path.
PDF Research Methods in Software Engineering
Research proposals need to be validated either by formal proofs or by applying empirical methods (e.g. controlled experiments). Many authors have pointed out that the level of experimentation in software engineering is not satisfactory. The quantity of experimentation is too low as a lot of software engineering publications do not contain
Research scholar program
Research Merit: Faculty's proposed research is aligned with Google Research interests, innovative, and likely to have a significant impact on the field. Proposal Quality: The research proposal is clear, focused, and well-organized, and it demonstrates the team's ability to successfully execute the research and achieve a significant impact.
Proposal for Master Thesis in Software Engineering
Proposal for Master Thesis in Software Engineering. S. Stein. Published 2005. Computer Science. TLDR. In this study a small German software company with 35 employees at 2 different locations (Halle and Chemnitz, both Germany) is studied and it is found that from the quality system point of view the company is immature. Expand.
Project proposal
Department of Computer Science and Technology. William Gates Building. JJ Thomson Avenue. Cambridge, CB3 0FD. Early in Michaelmas Term you need to submit a project proposal that describes what you plan to do and how you plan to evaluate it. In order to help with this process, you are assigned two Project Checkers, who, together with your ...
Examples of Research proposals
Research proposals. Your research proposal is a key part of your application. It tells us about the question you want to answer through your research. It is a chance for you to show your knowledge of the subject area and tell us about the methods you want to use. We use your research proposal to match you with a supervisor or team of supervisors.
Journal of Software Engineering Research and Development
Actor's social complexity: a proposal for managing the iStar model. Complex systems are inherent to modern society, in which individuals, organizations, and computational elements relate with each other to achieve a predefined purpose, which transcends individual goals. ... Citation: Journal of Software Engineering Research and Development ...
2024 Research Software Engineer Call For Proposals
The 1-page letter should explain how your team would benefit from increased expertise in research software engineering. Following an initial screening process, Curt Hillegas and Ian Cosden will hold "town hall" meetings to discuss the next step, namely, the preparation of a full proposal, which will be due on Wednesday, May 1, 2024.
How to Write a Software Proposal (With Examples)
5 software proposal writing tips. To rock your software proposals, try these smart tips. 1. Include images and videos alongside text. Multimedia content can help seal the deal. Including images in a SaaS proposal boosts the closing rate by 23%, while including at least one video will bump up the closing rate by 41%.
Engineering Proposals: Free Template + 12 Proposal Writing Tips
Expert Engineering Project Proposal Tips. To maximize your chances of winning projects with your engineering proposals, here are 12 proposal writing tips to keep in mind when crafting your next engineering proposal: 1. Read the RFP Multiple Times.
Undergraduate research in software engineering. An experience and
Table 1: Undergraduate research projects completed between 2015 and 2022. 3.2 The undergraduate research process. With some minor variations along the last years, the whole pro cess for ...
Software Project Proposal
In recent years, massive IT innovations led to economic growth and increased competition among companies in the industry. And because people expect a new software design and development each year, software experts and engineers must undergo thorough professional project planning to survive. With a sound project plan, IT experts and professionals can then prepare a written project proposal and ...
Engineering Project Proposal
Here are some necessary information that are commonly found in an engineering project proposal: 1. Have a proposal statement that can define the scope and limitations of the engineering project proposal. You have to develop a limit to what you are proposing so that project expectations can be set.



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Top 10 Software Engineer Research Topics for 2024