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Generative AI (GenAI) tools are increasingly being adopted in software development as productivity aids, since there is evidence that GenAI tools can improve individual aspects of productivity. However, productivity is multidimensional; accelerating one aspect of work may simply shift effort to another. In this paper, we investigate how GenAI adoption affects different dimensions of developer productivity. We surveyed 415 software practitioners to understand how they perceive productivity changes associated with AI adoption, using the SPACE framework (Satisfaction and well-being, Performance, Activity, Communication and collaboration, and Efficiency and flow). Our results reveal systematic redistribution of effort across SPACE dimensions. While frequent GenAI users reported faster task completion and higher output volume, these gains were offset by increased code review burden, persistent cognitive load from output verification, and unchanged collaboration patterns. We further provide an empirical mapping between the challenges perceived by developers and potential strategies to mitigate them. Overall, our findings suggest that, at the current stage of GenAI adoption, perceived productivity gains may be spurious -- surface-level acceleration, often accompanied by redistributed effort and hidden costs. 

Open Source Software (OSS) communities face a wave of uncertainty as Generative AI (GenAI) rapidly transforms how software is created, maintained, and governed. Without clear frameworks, communities risk being overwhelmed by the complexity and ambiguity introduced by GenAI, threatening the collaborative ethos that underpins OSS. To address this gap, we present a Socio-Technical System (STS)-guided conceptual framework that applies McLuhan's Tetrad as an analytic lens to articulate how GenAI reshapes the socio-technical dynamics of OSS development. Through a scenario-based exploration across four components of the STS-guided framework—software practices, documentation, community engagement, and governance—we identify plausible socio-technical impacts and outline a corresponding Roadmap for sustaining OSS communities in the Age of GenAI. This Roadmap will enable OSS researchers and practitioners to interpret emerging transformations, anticipate challenges, and design interventions that foster long-term community resilience. By adopting this framework, OSS leaders and researchers can proactively shape the future of their ecosystems, rather than simply reacting to technological upheaval. 

Open-source software (OSS) community managers face significant challenges in retaining contributors, as they must monitor activity and engagement while navigating complex dynamics of collaboration. Current tools designed for managing contributor retention (e.g., dashboards) fall short by providing retrospective rather than predictive insights to identify potential disengagement early. Without understanding how to anticipate and prevent disengagement, new solutions risk burdening community managers rather than supporting retention management. Following the Design Science Research paradigm, we employed a mixed-methods approach for problem identification and solution design to address contributor retention. To identify the challenges hindering retention management in OSS, we conducted semi-structured interviews, a multi-vocal literature review, and community surveys. Then through an iterative build-evaluate cycle, we developed and refined strategies for diagnosing retention risks and informing engagement efforts. We operationalized these strategies into a web-based prototype, incorporating feedback from 100+ OSS practitioners, and conducted an in situ evaluation across two OSS communities. Our study offers (1) empirical insights into the challenges of contributor retention management in OSS, (2) actionable strategies that support OSS community managers’ retention efforts, and (3) a practical framework for future research in developing or validating theories about OSS sustainability. 

Novice programming students frequently engage in help-seeking to find information and learn about programming concepts. Among the available resources, generative AI (GenAI) chatbots appear resourceful, widely accessible, and less intimidating than human tutors. Programming instructors are actively integrating these tools into classrooms. However, our understanding of how novice programming students trust GenAI chatbots-and the factors influencing their usage-remains limited. To address this gap, we investigated the learning resource selection process of 20 novice programming students tasked with studying a programming topic. We split our participants into two groups: one using ChatGPT (n=10) and the other using a human tutor via Discord (n=10). We found that participants held strong positive perceptions of ChatGPT's speed and convenience but were wary of its inconsistent accuracy, making them reluctant to rely on it for learning entirely new topics. Accordingly, they generally preferred more trustworthy resources for learning (e.g., instructors, tutors), preferring ChatGPT for low-stakes situations or more introductory and common topics. We conclude by offering guidance to instructors on integrating LLM-based chatbots into their curricula-emphasizing verification and situational use-and to developers on designing chatbots that better address novices' trust and reliability concerns. 

Large Language Model (LLM) conversational agents are increasingly used in programming education, yet we still lack insight into how novices engage with them for conceptual learning compared with human tutoring. This mixed‑methods study compared learning outcomes and interaction strategies of novices using ChatGPT or human tutors. A controlled lab study with 20 students enrolled in introductory programming courses revealed that students employ markedly different interaction strategies with AI versus human tutors: ChatGPT users relied on brief, zero‑shot prompts and received lengthy, context‑rich responses but showed minimal prompt refinement, while those working with human tutors provided more contextual information and received targeted explanations. Although students distrusted ChatGPT’s accuracy, they paradoxically preferred it for basic conceptual questions due to reduced social anxiety. We offer empirically grounded recommendations for developing AI literacy in computer science education and designing learning‑focused conversational agents that balance trust‑building with maintaining the social safety that facilitates uninhibited inquiry. 

Code reviews are an ubiquitous and essential part of the software development process. They also offer a unique, at-scale opportunity for teaching developers in the context of their day-to-day development activities versus something more removed and formal, like a class. Yet there is little research on effective teaching through code reviews: focusing on learning for the author and not just changes to the code. We address this gap through a case study at Google: interviews with 14 developers revealed 12 patterns and 15 anti-patterns in code reviews that impact learning. For instance, explanatory rationale, sample solutions backed by standards, and a constructive tone facilitates learning, whereas harsh comments, excessive shallow critiques, and non-pragmatic reviewing that ignores authors' constraints hinders learning. We validated our qualitative findings through member checking, interviews with reviewers, a literature review, and a survey of 324 developers. This comprehensive study provides an empirical evidence of how social dynamics in code reviews impact learning. Based on our findings, we provide practical recommendations on how to frame constructive reviews to create a supportive learning environment.