software development team leads

Abstract: Generative AI (GenAI) makes open source software (OSS) contributions cheaper to produce, and it makes them say less about the people who produced them. In OSS mentoring, that matters: mentors judge what newcomers understand from their work, and newcomers learn from mentors' feedback. We studied how GenAI is changing mentoring in Google Summer of Code (GSoC), a program in which today's mentees can become tomorrow's mentors. We screened every thread on GSoC's restricted mentors' mailing list from November 2022 through March 2026 and interviewed 15 mentors and organization administrators. GenAI helped with onboarding, coordination, and first-pass review, but proposals, entry contributions, and mentees' answers became weaker evidence of what newcomers could do. Mentors made up the difference by checking, and that checking drew on the same time and goodwill the program needs to renew itself; one organization had withdrawn from GSoC. Responses ranged from banning GenAI to accepting it, and the boundary an organization drew determined who did the checking. We propose AIM, a decision framework that links acceptable GenAI use, integration into the mentoring pipeline, and mentoring capacity, to help organizations choose responses they can sustain.

Abstract: Generative AI can produce code, tests, documentation, and technical explanations that resemble artefacts traditionally used as evidence of student competence, weakening the inference from artefact quality to the engineering capability behind it. We report the redesign and first deployment of a 13-week, AI-permissive, software-intensive engineering course in which students may delegate substantial work to AI while retaining responsibility for planning, verification, review, integration, and explanation. The redesign couples an engineering accountability workflow, which structures human-AI work and preserves human decision authority, with an assessment evidence architecture comprising six complementary channels: product, process/origin, verification, explanation, reflection, and individual assurance. A collect--cross-check--escalate (CCE) reasoning logic connects these channels rather than treating any single artefact or trace as sufficient evidence of competence. By the end of Week 11, the deployment involved 87 students organised into 17 teams, with 957 individual weekly Git repositories and 34 group-project Git repositories supporting repeated assessment and engineering activity. The first deployment yielded four practical lessons: AI assistance changes where rubric criteria carry evidential weight; multiple evidence channels require deliberate cross-checking and meaningful complementarity; the evidential strength realised in practice may differ from that intended in the assessment design; and automation scales evidence collection more readily than interpretation. The contribution is a conceptually grounded, deployed design for making engineering responsibility and competence more visible when AI can perform substantial artefact production.

Abstract: Online knowledge communities rely on a division of epistemic labor between users who seek information and those who produce it. Generative AI may blur these roles, but how it reallocates knowledge-seeking and knowledge-producing activities and reshapes the nature and returns of participation remains unclear. We examine changes in question-asking and answering among Stack Overflow user groups, focusing on consumers and producers, following the release of ChatGPT. Consumers shifted from asking questions to producing answers, but their answers were less likely to be accepted relative to the pre-ChatGPT period, suggesting that increased production did not yield equal standing in the community. By contrast, producers did not change the number of questions they asked, but increasingly asked about novel and emerging topics. Although they produced fewer answers, their answers received greater recognition. These findings show that access to generative AI does not necessarily translate into equal opportunities for successful participation.

Abstract: Generative artificial intelligence has moved from a disruptive novelty to a recurring part of software-development and computing-education workflows, while software agents are beginning to act across repositories, command lines, browsers, tests, and other tools. The educational problem is no longer whether students should be allowed to generate code, but whether software-engineering (SE) programs can preserve and assess human understanding while preparing students to work responsibly with increasingly capable AI systems. This paper presents a structured integrative review of research and practice from 2023 through 23 September 2026, supplemented by established work on AI literacy, technical debt, and human-AI collaboration. The evidence supports a conditional conclusion: GenAI can improve access to explanations, feedback, practice, and short-term task completion, but learning outcomes depend on prior knowledge, scaffolding, verification, task design, and assessment. We examine student help-seeking and authorship, faculty assessment and policy demands, and the wider SE risks created when generated artifacts persist across repositories, teams, architectures, and deployed systems. We extend the concept of comprehension debt: the deferred learning and maintenance cost that arises when AI-assisted production outpaces a learner's or team's ability to explain, test, modify, and justify the resulting software. We then refine the AI-Augmented Software Engineering Education (AASEE) framework into five non-linear integration levels and four cross-cutting evidence obligations: explain, verify, modify, and account. The framework links delegation to evidence, governance, and recovery mechanisms appropriate to its consequences.

Abstract: Pull requests (PRs) underpin pull-based software development by enabling distributed code review and collaborative contribution in open-source projects. Yet many become inactive before integration and are eventually abandoned or closed, wasting contributor and maintainer effort. Although prior work has examined PR abandonment and review delays, less is known about the contribution types, discussion-level barriers, and post-stalling collaboration patterns associated with inactivity. We investigate which PR types most often stall, why inactivity occurs from authors' and reviewers' perspectives, and how stalling relates to later contributor and reviewer engagement. We analyzed 14,234 stalled PRs and 164,562 review comments from 19 popular GitHub repositories using stale-bot workflows. An LLM-based voting classifier categorized PRs by contribution type, while quantitative analysis was combined with qualitative coding of general and inline review discussions. Feature-enhancement and issue-fixing PRs formed the largest share, together exceeding 77% of classified stalled PRs. General comments linked stalling mainly to communication and coordination breakdowns, including missing interaction, delayed feedback, and unclear follow-up. Inline comments showed that inactivity does not always reflect disengagement: many PRs were blocked by technical or dependency issues, including failing checks, configuration problems, compatibility concerns, and environment mismatches. Only 39.56% of contributors later submitted another PR, and reviewer re-engagement with the same contributors was approximately 21%. PR inactivity is a socio-technical coordination problem involving communication, technical readiness, review ownership, and automation practices. We recommend type-aware triage, clearer review feedback, explicit ownership of next actions, CI blocker management, and cause-aware stale-bot interventions.