A framework for describing documentor mindsets, and their associated considerations across the three dimensions is contributed, surface the need to reconsider existing perspectives of the documentation creation process, including the role of the documentor and metrics to measure documentation quality.
The work identifies five distinct stages of the documentation review process: self review, technical review, editorial review, play testing, and post-publication feedback, and draws on practitioners with distinct expertise to address quality across content, presentation, and user experience.
Avinash Bhat, Ian Arawjo, Disha Shrivastava et al.· 1 citation· ⚡1
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.
Contributors to Open Source Software (OSS) projects are vital to maintaining the health of both communities and projects. However, the number of projects experiencing core contributors'disengagement has increased to the point that it risks the projects'survival. Finding new contributors to replace the workforce is challenging and time-intensive due to several factors, including a lack of precise knowledge about potential candidates'competences, which may need to be confirmed through interviews and exams. Previous studies provided indications of the contributor's competences, although they lack depth in understanding competence levels, which can result in poor knowledge about the contributor's capabilities. To address this gap, we assess contributors'competence by collecting code metrics related to source code from contributions. We also propose a competence model able to predict the competence level required to solve tasks. By properly assessing contributors'competences, we can identify and train candidates to replace core contributors. Our replication package, including code, data, and documentation, is available at https://doi.org/10.5281/zenodo.21605122
Sabahat Younas, Marcia C. Moraes, Fabio Santos· 0 citations
Interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023 offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.
Benyamin T. Tabarsi, Heidi Reichert, Sam Gilson et al.· Empirical Software Engineeri...· 22 citations· ⚡1
Artificial intelligence is reshaping open source software (OSS) contribution by lowering the cost of producing code, documentation, issue reports, and review interactions. This creates opportunities for broader participation, but also disrupts how maintainers assess contributor effort, competence, and accountability. In response, OSS projects are beginning to regulate AI-mediated contribution through contribution guidelines and other project documentation. This paper presents an empirical study of these emerging policies. We analyze project policies on AI-mediated contributions by evaluating their underlying rationales, rules, and expectations. Our analysis shows that these policies seek to protect scarce maintainer attention, preserve accountability, sustain meaningful review interactions, address legal and quality concerns, and maintain pathways for newcomer learning. Based on these findings, we introduce the AI Contribution Governance Framework, which organizes recurring concerns and governance mechanisms across projects. The framework helps OSS communities develop AI contribution policies and provides researchers with a vocabulary for studying how AI is changing collaborative software production.
G. Robles, D. Germán· 1 citation
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