When Bots Join the Team: Bot Adoption and the Institutional Fabric of Open-Source Software Projects

When leaders back the bots: AI affordances, empowerment, and developer innovative work behavior.

As AI-assisted coding, debugging, and code review tools become integrated into the software development workflow, a key question is raised: under what circumstances do these tools encourage innovative work behavior instead of simply automating routine tasks? Based on the proactive motivation model, this study focuses on the relationship between the affordance of AI technology and innovative work behavior of software developers in China, and discusses the psychological and contextual mechanisms underlying the relationship. We propose that AI technology affordances, i.e., interactivity, personalization, convenience, and social presence, enhance innovative work behavior through two mediators, i.e., digital self-efficacy and psychological empowerment. We also argue that leader support for the use of AI increases the positive effects of AI technology affordances on both digital self-efficacy and psychological empowerment. Survey data from 411 software developers in four major cities of China were analyzed using structural equation modeling. Results support all hypothesized relationships, which suggests that the AI technology affordances promote innovative work behavior indirectly through digital self-efficacy and psychological empowerment and that these relationships are stronger when leader support for AI use is high. The study identifies AI technology affordances and AI-specific leadership support as important levers for developing innovation in AI-intensive software development teams.

When AI Joins the Team! A Model of How AI Adoption Relates To Social Patterns in Software Engineering Teams

Context: The growing adoption of AI-assisted development tools is changing how software teams collaborate, share knowledge, and coordinate, yet its consequences for team social dynamics remain largely unexplored. Gap: It is unclear whether AI adoption is associated with an increase or reduction in community smells,socio-technical anti-patterns reflecting coordination and communication breakdowns,and through which mechanisms. Method: Grounded in Transactive Memory Systems (TMS) theory, we validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Specialization and Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools. Community smell constructs were derived from the literature and validated through expert surveys and factor analysis. Results: AI adoption relates to community smells not in a single way, but through mechanisms depending on the work. In specialization work, AI is associated with higher knowledge-sharing peer interaction, which is in turn associated with fewer smells. In coordination work, AI is directly associated with higher communication quality, complementing rather than replacing human interaction. Contributions: We provide an empirically validated, TMS-grounded model showing that the AIcommunity-smell relationship is contingent on the type of collaboration, with a reusable instrument and evidence-based implications for research and practice.

From Social Coding to Agentic Coding: Productivity and Relational Reconfiguration in Open-Source Communities

Open-source software communities are a form of digital public infrastructure that not only produces code, but also generates public knowledge and interpersonal relationships through visible collaboration. Generative coding agents (CAs) are an advanced tool to improve development efficiency while shifting part of activities from public human interaction to private human-agent loops. We study this shift using an LLM-based multi-agent simulation initialized with real GitHub data from 1,084 active developers and their repository relationships. After a warm-up with historical commits, we branch the same community state into parallel No-CA and CA conditions for 4-week simulations. CA introduction increases planned and completed tasks by 34.0% and 39.0%, respectively, and reduces median completion time from 45 to 20 minutes. However, adoption reaches only 26.0%, and the gains concentrate among developers who are already more active and well connected. CAs also restructure task execution pathways. Direct human-human interaction declines from 32.4% to 11.6%, while CA-involved modes increase to 57.3%, including 40.3% completed through CA-assisted self-loops. Public knowledge generated under CA condition also provides less support for later tasks. On a standardized retrieval benchmark, the CA corpus achieves 22.3% knowledge coverage, far below the 81.1% achieved by the real-human corpus, and requires more retrieval steps with a lower success rate. These results reveal a productivity-public knowledge tension: coding agents increase technical production, but more work shifts to agent-mediated or private loops, leaving public records less useful to future contributors.

How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study

This paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face, and describes a seven-stage workflow and five process shifts, including a move toward evaluation-driven development.

and Rainbows: Motivation and

AI-Driven Leadership Assessment and Organizational Trust: Evidence-Based Frameworks for Technology Sector Transformation

What conditions does AI need to meet to improve leadership and trust inside a technology firm rather than damage them? That question is the starting point of this paper. We pulled together recent research alongside production case studies from six major firms (Google, Microsoft, IBM, Salesforce, Adobe, LinkedIn) and tried to separate the marketing claims from what these tools deliver in practice. Some numbers hold up well: AI-driven leadership assessment improves prediction accuracy by 80%, and hiring costs drop 30–50% in firms where the technology is deployed well. Other claims are conditional. Returns only show up once the cultural work is done. We trace four maturity stages and find that 91% of what blocks adoption is cultural rather than technical. Firms that get past those blockers report 40–75% less time spent on recruitment, 95% accuracy in spotting employees about to leave, and 14.9% higher leadership-performance scores. Trust sits underneath every one of these outcomes. Where it already exists, AI adoption runs 21 percentage points smoother. Where it does not, AI makes things worse, not better. The remainder of the paper sets out the conditions under which AI strengthens leadership instead of eroding it.