software development in thesis

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Using Rapid Application Development for Software Development Projects

Degree type.

• Master of Science
• Computer and Information Technology

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• West Lafayette

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Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

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How to evaluate a software implementation for master thesis

I have developed a software to meet a specific requirement in a research field. There is only one such software developed so far, mine is second.

In the thesis, I explain the implementation of the software. However, I feel like I also need a "Results" chapter. Most of the master theseses I have gone through from various disciplines have such a chapter. Looks like it is a must.

My software generates tangible models, storing a specific information, to be manufactured with various methods, such as CNC or 3D printing.

Output of the software should be considered as results. However, I don't want to conduct a cheesy case study with a bunch of people and publish it as a scientific result. An extensive, thorough study is takes more than 6 months and neither I have that much time nor the the knowledge/experience to conduct such a case study.

For example a friend of my implemented a software for twitter clustering and analyzing. The results are quite straightforward with his software. Run the tool, get the results and interpret them. Done.

I am not sure how to evaluate the results. Models are manufactured successfully, whereas that does not look like a result to me.

How would you approach this question for a possible solution? What my alternatives can be?

• 1 In computer science, it is indeed typical to have an evaluation/validation section in a master's thesis, but there is no need for this section to be named "Results". You can choose a name that fits best. –  lighthouse keeper Commented Jan 12, 2017 at 11:30
• 4 The "cheesy case study with a bunch of people", as you call it, is often a valuable part of the thesis in that it shows that you are not only capable of coding, but also of designing the material and setting for such a study. The point is not to generate a full-fledged "scientific result", but to show that you could generate such a result if given sufficient funding and time. Furthermore, while a thorough, statistical analysis is beyond what you can do in a Master thesis, do not underestimate the impact of listing at least some "first impressions" uttered by users other than yourself. –  O. R. Mapper Commented Jan 12, 2017 at 11:33

2 Answers 2

The goal of your thesis was to create a tool that meets certain requirements. Consequently, your evaluation should show that your tool satisfies these requirements .

One way to organize your thesis is to have a section called "Requirements", where you walk through all requirements detailedly. For example, you could have following requirement with sub-requirements:

• R1.1: Brown cats : the model generation works for brown cats
• R1.2: Black cats : the model generation works for black cats
• R1.3: Checkered cats : the model generation works for checkered cats

Then you have another section called "Validation" where you walk through the requirements a second time, this time discussing for each requirement how you satisfied it.

• R1.1 : for 4 sample cats, the tool was able to produce a model (see Fig. 1)
• R1.2 : for 5 sample cats, the tool was able to produce a model (see Fig. 2)
• R1.3 : for 3 sample cats, the tool was able to produce a model (see Fig. 3)

• 1 Thank you so much. That sounds reasonable and clarifies the questions –  user1449456 Commented Jan 12, 2017 at 11:24

If someone asked you: "does your software work"? How would you prove to them that it does? How did you test the software and proved to yourself that the software works as intended? That could be the basis of a results section.

• So, manufactured tangibles through software can be considered as results? –  user1449456 Commented Jan 12, 2017 at 11:21

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4 ways ai drives change in software development.

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Dimitar Dimitrov is the founder and Managing Partner at Accedia , a leading European IT services company.

Slow and insufficient definition of requirements, time-consuming coding and error-prone bug detection are just some of the issues that teams face in the software development life cycle (SDLC). Consequently, a paper published by the Harvard Business Review says that only 35% of software development projects are completed successfully. As we navigate through 2024, the potential for AI to improve every phase of the SDLC—from planning and design to coding, testing, deployment and maintenance—has never been more evident. Still, employees tend to resist using AI technologies due to the lack of familiarity. Below, I provide valuable insights from my firsthand experience into how businesses can use their power to increase precision, efficiency, speed and growth.

Revamping The Discovery Process

AI-powered tools like ChatGPT and Google Gemini are often used to help write user stories and requirements. They can improve the discovery process by automating data collection and analysis and identifying user needs through advanced analytics. Documenting user feedback and requirements can be enhanced by leveraging natural language processing to analyze large amounts of data and pinpoint critical patterns and trends.

In our work, for instance, these tools assist us in modeling and simulating scenarios, forecasting potential challenges and refining the requirements and project scope more accurately and timely. Additionally, we use multimodal inputs from speech, images and text, where generative AI can help personalize solutions and better understand the customer profile.

Streamlining The Design Phase

AI has gained momentum in the design process as well. From user research and analysis and flow diagram generation to UX design assistance and usability testing, AI has the power to streamline the design process by recognizing patterns and predicting user behavior. AI-driven tools can enable more precise personalization and automate aspects of the design process. Utilizing ML models can enhance accessibility by scanning and analyzing content and identifying issues in real-time, which allows proactively tackling accessibility barriers in advance, leading to outperforming competitors by 50%, according to Gartner .

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Some exciting new tools on the market enable our designers to perform tests and collect data on user interactions and heat maps, pinpointing improvement opportunities and allowing the team to create a more user-friendly and user-centric product. In their everyday work, our team uses Adobe Firefly, Dall-E 3, Uizard and Colormind, which automate and improve the design process from color selection to prototyping, visual generation and personalization—the ultimate goal when it comes to creating a new product.

Eliminating Project Management Bottlenecks

A large number of software development projects need more cross-functional communication or outdated project and team management tools, which leads to technical challenges, poor efficiency, misalignment, scope creep and more. Using legacy tools such as Excel spreadsheets can cause issues with the quality of the product, budget overruns or ineffective resource allocation. On the other hand, the lack of effective communication and collaboration ultimately can create barriers between the research and development phases. Thus, according to Gartner , by 2030, 80% of the Project Management tasks will be performed utilizing AI. Being already on that path, our Project Managers have already noticed having more time to focus on what they do best—lead teams and help them perform to the best of their abilities while leaving tasks such as documentation, reporting and even writing personas and acceptance criteria primarily to tools such as Copilot or ChatGPT.

ML-driven allocation and prioritization, additionally, can lead to eliminating human biases from the decision-making process, bettering talent based on their skill set, assessing risks and allocating and predicting future workforce needs. Automating the collection and analysis of user stories, on the other hand, will eliminate inconsistencies, duplicates and complexities. Furthermore, we can see a significant improvement in risk management where ML and big data allow us to predict risks, continuously monitor project parameters and recommend mitigation strategies when needed. Last but not least, AI tools that monitor the progress of the projects, help us address potential crises and ensure compliance with various policies will become indispensable. Bear in mind these are just a handful of examples of how AI tools such as ChatGPT can streamline project management and execution.

Payabli is building payment management tools for software startups

Joseph Phillips and William Corbera, both of whom come from entrepreneurial backgrounds, have been friends for over a decade.

Corbera co-founded RevoPay, a payments processing platform that was acquired by payments solutions firm OSG in 2022. Phillips, for his part, led the national sales team at Seamless before heading up sales at ServiceTitan, a web-based management tool for construction contractors.

In 2020, Phillips and Corbera — having worked in payments-related jobs for a number of years — decided to team up to found their own payments-focused venture called Payabli . Payabli builds the infrastructure that allows companies, specifically software companies, to embed and facilitate payments through APIs.

“Payabli builds payment acceptance and issuance solutions [and] payment operations tools,” Corbera told TechCrunch. “We make software companies payments companies by giving them payment-facilitating capabilities without the heavy lift, administrative burden and exorbitant cost of becoming a payment facilitator.”

Payabli is essentially trying to disrupt traditional payments facilitators like Stripe, Adyen and Paytrix : Companies that let customers accept electronic payments using their platforms. Payments facilitators act as middlemen between businesses and their banks, delivering the back end for payments processing.

Payabli offers the standard array of “pay-in” payment acceptance tools, including tools to let a company’s clients make recurring or scheduled payments or request invoices. But it also provides “pay-out” tools to help companies themselves pay vendors and suppliers, like virtual credit cards, physical checks and bank integrations.

Payabli’s services extend to various “payment operations” products, as well, including products designed to mitigate risk and fraud, handle disputes and compliance and facilitate underwriting.

“Payments and other fintech programs are the lowest-hanging fruit for software companies to unlock new revenue and create stickier, more valuable customer relationships,” Corbera said. “This is not only true for software companies, but any entity that coordinates money movement between payers and recipients.”

Payabli’s go-to-market approach has won approval from VCs, who’ve poured a substantial amount of capital into the startup. Payabli this week announced that it raised $20 million in a Series A funding round led by QED with participation from TTV Capital, Fika Ventures and Bling Capital, bringing the company’s total raised to $32 million at a “nine-figure” valuation. (Corbera wouldn’t reveal the exact amount.)

Payabli has around 60 customers, Corbera said, adding that revenue grew 3x over the past 12 months to “seven figures.”

“The new round of funding will be used to drive further product innovation, reinforce security and scalability, fuel new customer acquisition and empower existing software partners to integrate and activate total processing volume easier and faster,” Corbera said. “We had over 16 months of runway left when we raised, but we chose to raise opportunistically to further accelerate our growth and take on some large enterprise customers.”

Payabli, based in Miami, has 49 employees and expects to have nearly 70 by the end of the year.

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Serpentinized ultramafic rocks of Evia Island (Greece) as potential reservoirs for CO2 mineralization based on a petrological research

• Karkalis, Christos
• Magganas, Andreas
• Koukouzas, Nikolaos
• Koutsovitis, Petros

In the island of Evia (Aegean, Greece), big peridotitic masses crop out in the central and north parts displaying different extents of serpentinization. These rocks were frequently subjected to CO2-metasomatism [1,2] forming magnesite deposits that have been exploited for several years for industrial purposes (currently by Grecian Magnesite and TERNA Mag companies). The non-carbonated ultramafic rock types mostly appear in the broader areas of Pagondas, Dafni, Psachna, Makrimalli, Artaki and Vatondas. These are predominantly divided into serpentinized harzburgites/lherzolites and serpentinites, whereas scarce occurrences of garnet-bearing serpentinites are rarely evident close to the Pagondas locality in spatial association with rodingites [3]. The mineralogy [mostly serpentine ± (clino- and ortho-) pyroxenes ± olivine ± garnet] and geochemistry (i.e. high MgO, FeO and occasionally CaO contents) of representative rock samples from the Pagondas and Psachna-Makrimalli regions indicate that they can react with carbonated water in order to crystallize carbonate mineral phases. This is also shown by thermodynamic calculations with PERPLE X software [4], which simulate the natural carbonation of Central Evia ultramafic rocks, revealing that they have the physicochemical potential to form specific carbonate minerals (siderite, magnesite and calcite) at T ≤ ~390oC. Thus, it is concluded that in Central Evia the petrological properties and relatively high quantities of serpentinized peridotites, offer a potentially viable option for their exploitation as reservoir rocks during in-situ CO2-storage. These non-carbonated ultramafic outcrops are located proximal to: (a) the Western and Eastern coastline of Evia Island (i.e. ~6 km far from the Evoikos Gulf and ~20 km far from the Aegean Sea), (b) the Central Evia groundwaters [5], (c) the Edipsos hot-springs in Northern Evia [6] and (d) areas with industrial activities close to the city of Chalkida. Hence, their geographical distribution could contribute to the financial viability of such storage scenarios lowering the logistic costs and contributing to sustainable development through actions for the mitigation of climate change.References: [1] Karkalis, C. 2022: PhD-Thesis National and Kapodistrian University of Athens, Department of Mineralogy and Petrology, 371p; [2] Grieco et al., 2023: Minerals, 13(2), 159; [3] Karkalis et al., 2022: Lithosphere, 2022 (1): 9507697; [4] Connolly, J.A.D., 2009: Geochem Geophys 10, Q10014; [5] Voutsis et al., 2015: J. Geochem. Explor., 159, 79-92; [6] Kanellopoulos et al., 2016: Proc. 14th Intern. Congress Geol. Soc. Greece, Thessaloniki, May 2016, 50(2), 720-729

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Reinforcing Coastlines: Adaptive Reshaping of Coastal Terrain Informed by Natural Sedimentation Patterns for Mangrove Root Systems

 Coastal Terrains have been recognized by scientists over the years as hazardous and dynamic zones. As we face  challenges over global warming, with shifts in climate patterns, coastal soil erosion is a pressing concern for various  ecological and marine systems. This problem has an economic, ecological, and social dimension. Coastal  infrastructures around high value economic zones have been reduced to concrete embankments and are interpreted  as structural means to force a boundary. The limitation of reactive, action-based shoreline management plans can be  mitigated by implementing natural ecosystems and their structural qualities for developing future interventions.  Various coastal morphologies, ranging from reef systems to mangroves, gravel beaches, sand deposition areas, and  salt marshes, showcase diverse ecological adaptations to dynamic natural forces. The experiment addresses the  evaluation of mangrove root systems and their ecological models, systems, and elements of nature to solve the  dynamic problems at the coast.  

This research framework focuses on gaining a deeper understanding of natural sedimentation as a regenerative tool  for reinforcing coastal areas, drawing inspiration from the growth patterns of mangrove systems. The study highlights  the importance of adaptive solutions for coastal landscapes, acknowledging the dynamic nature of these environments  and leveraging insights from natural soil accumulation patterns to enhance the resilience of coastal landforms in the  future. The methodology primarily involves two key exercises: Computational Fluid Dynamics (CFD) Analysis and  physical testing using simulation sand. Utilizing GIS data mapping of coastal terrain in combination with land loss  data enables the development of prediction models, and the mapping of ecological biomes and ecosystems against  these models while identifying the vulnerable coastlines. Additionally, experimental physical testing with simulation  sand contributes to the creation of a database detailing the topological shaping of the terrain, to achieve self?regenerative coastal reinforcement. 

The study is situated in Charlotte Bay, Florida by mapping one of the biggest mangrove biomes along the Gulf of Mexico at Port Charlotte which is considered the Parent site with a reserve of its propagules floating with the currents  in the bay. The Target site is Charlotte Harbor, along the coast of which a cluster of profiles would be embedded. The  experiment concluded that sand sedimentation strategies along the coastline through minimum human intervention,  that is, embedding profiles of minimum diameter 5.13 mm, can be achieved to form catchment zones for mangrove  propagules in circulation at a target close to a parent site. The study also cements the role of mangrove forests as a  strategy for coastal reinforcement by predicting the effects of the experiment over the course of twenty plus years.  

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• Master's Thesis
• Architecture

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• Master of Science in Sustainable Design (MSSD)

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