arXiv subjects

Shashiwadana Nirmani

Publications and source records attributed to Shashiwadana Nirmani.

3 recordsLinked to original sources

arXiv2026

What Motivates Whom? A Survey of Newcomers to OSS and Experienced OSS Practitioners

Open source software (OSS) development continues to expand, yet software practitioners often struggle to select suitable projects, leading to inefficient onboarding and disengagement. Understanding how contributors select OSS projects is important for supporting contributors onboarding, engagement, and long-term participation within OSS communities. This study investigates contributors' project-selection preferences in OSS projects and examines how these preferences correlate with contributors' motivations and demographic backgrounds. Through an online survey of 208 practitioners, we found that demographic factors, such as age, gender, and the OSS role they held, significantly correlate with their motivations. Additionally, preferences for project characteristics such as project age, development stage, and documentation quality vary based on specific motivations. Importantly, our findings are presented through a comparative lens, analyzing the responses of newcomers to OSS and experienced OSS practitioners separately to uncover their distinct preferences. Lastly, we explore software practitioners' perspectives on how existing recommendation systems could better support project selection and align with their motivations. By disentangling the unique needs of newcomers to OSS and OSS practitioners, our findings provide insights for researchers, OSS project owners, and software practitioners to improve contributor onboarding, engagement, and retention, while also informing future project recommendation systems and improving the OSS ecosystem.

cs.SE↗

arXiv2026

Beyond Code: Empirical Insights into How Team Dynamics Influence OSS Project Selection

Open-source software (OSS) development relies on effective collaboration among distributed contributors. Yet, current OSS project recommendation systems primarily emphasize technical attributes, overlooking the collaboration and community aspects that influence contributors' decisions to join and remain in projects. This study investigates how team dynamics within OSS communities influence project selection and how these preferences vary across contributors' motivations. We conducted an online survey with 198 OSS practitioners, combining quantitative and qualitative analyses to capture contributors' perceptions of team dynamics. The results reveal that communication-related team dynamics such as responsiveness, tone, and clarity of replies are consistently prioritized across practitioners. However, the relative importance of these team dynamics differs according to contributors' motivations. For instance, practitioners motivated by gaining reputation or networking preferred inclusive project communities that encouraged diverse participation. These findings highlight that understanding how team dynamics align with contributors' motivations provides valuable insights into practitioners' project selection behaviour. Those insights can inform the design of future human-aware project recommendation systems that better account for social collaboration quality and motivational fit.

cs.SE↗

arXiv2025

A Systematic Literature Review on Task Recommendation Systems for Crowdsourced Software Engineering

Crowdsourced Software Engineering (CSE) offers outsourcing work to software practitioners by leveraging a global online workforce. However, these software practitioners struggle to identify suitable tasks due to the variety of options available. Hence, there have been a growing number of studies on introducing recommendation systems to recommend CSE tasks to software practitioners. The goal of this study is to analyze the existing CSE task recommendation systems, investigating their extracted data, recommendation methods, key advantages and limitations, recommended task types, the use of human factors in recommendations, popular platforms, and features used to make recommendations. This SLR was conducted according to the Kitchenham and Charters' guidelines. We used both manual and automatic search strategies without putting any time limitation for searching the relevant papers. We selected 65 primary studies for data extraction, analysis, and synthesis based on our predefined inclusion and exclusion criteria. From the results of the data analysis, we classified the extracted data into four categories based on the data extraction source, categorized the proposed recommendation systems to fit into a taxonomy, and identified the key advantages and limitations of these systems. Our results revealed that human factors play a major role in CSE task recommendation. Further, we identified five popular task types recommended, popular platforms, and their features used in task recommendation. We also provided recommendations for future research directions. This SLR provides insights into current trends, gaps, and future research directions in CSE task recommendation systems

cs.SE↗