Software Technology and Software Development
Permanent URI for this collectionhttps://hdl.handle.net/10125/112565
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Item type: Item , Investigating the Pareto Principle in Student Software Engineering Team Projects(2026-01-06) More, Aditi; Buffardi, KevinThe Pareto principle, or 80/20 rule, has been observed in open-source and professional software projects, indicating that ”vital few” 20% of contributors account for at least 80% of the outcomes. However, its applicability to academic software engineering teams remains uncertain. This study investigates contribution inequality among student developers in software engineering teams by analyzing data-mined GitHub activities. Using the Hoover Index and Lorenz Curve, we analyzed whether a small subset of students performed a disproportionate share of the work to their teams. Contrary to the classic Pareto distribution, our results show that out of 94 teams, only six teams (6.4%) met the 80/20 criterion—–where their top 20% of contributors produced at least 80% of that team’s work–—while the remaining 88 teams did not reach this threshold. We discuss potential academic factors such as grading incentives, peer review, and structured team dynamics that may mitigate contribution imbalance.Item type: Item , Understanding the Evolution of Serverless Computing: A Computational Literature Review(2026-01-06) Hamza, Muhammad; Capilla, Rafael; Smolander, KariServerless computing has enabled developers to execute the application logic without managing the underlying infrastructure. This abstraction offers automatic scaling, reduced operational costs, and rapid deployment. These benefits motivated researchers to explore its different characteristics and use cases in various domains such as machine learning and video processing. Thus, research in this area has expanded significantly in recent years. However, there remains a limited understanding of the topics currently being investigated and the directions future research should pursue. To this end, we conducted a Computational Literature Review (CLR) to examine the current research landscape of serverless computing. We applied topic modeling to identify key areas of focus and emerging themes within the literature. Our analysis revealed 40 distinct topics, which we mapped into 10 overarching themes. Finally, we discussed these themes and provided future research directions across each theme. The findings of this study help researchers understand the current landscape of serverless computing and highlight opportunities for future investigationItem type: Item , Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study(2026-01-06) Stray, Viktoria; Brandtzæg, Elias Goldmann; Wivestad, Viggo; Barbala, Astri; Moe, Nils BredeThis study investigates the real-world impact of the generative AI (GenAI) tool GitHub Copilot on developer activity and perceived productivity. We conducted a mixed-methods case study in NAV IT, a large public sector agile organization. We analyzed 26,317 unique non-merge commits from 703 of NAV IT's GitHub repositories over a two-year period, focusing on commit-based activity metrics from 25 Copilot users and 14 non-users. The analysis was complemented by survey responses on their roles and perceived productivity, as well as 13 interviews. Our analysis of activity metrics revealed that individuals who used Copilot were consistently more active than non-users, even prior to Copilot’s introduction. We did not find any statistically significant changes in commit-based activity for Copilot users after they adopted the tool, although minor increases were observed. This suggests a discrepancy between changes in commit-based metrics and the subjective experience of productivity.Item type: Item , Software Architecture for Essential and Accidental Uncertainty(2026-01-06) Harrison, Neil; Rudolph, George; Aldous, PeterSoftware systems must deal with various aspects of uncertainty. Some uncertainty comes from external factors, such as unreliable data transmission, while other uncertainty is inherent to the problem itself. We define these as accidental and essential uncertainty. For accidental uncertainty, there exists a transformation of the data which allows us to eliminate or mitigate the uncertainty. For essential uncertainty, there is no such transformation possible. Accidental uncertainty has been extensively studied, but essential uncertainty has not. We explore essential uncertainty in more depth, including its impact on software architecture. We examine different levels of essential uncertainty and associated architectural approaches, leading to a spectrum of uncertainty and architecture. We propose an informal process using uncertainty to guide the architectural process, and give examples.Item type: Item , Introduction to the Minitrack on Software Technology and Software Development(2026-01-06) Grønli, Tor-Morten; Kaindl, Hermann; Majchrzak, Tim A.