Adam Alami

Context: Free and Open Source Software (FOSS) communities’ sustainability, meaning to remain operational without signs of weakening or interruptions to its development, is fundamental for the resilience and continuity of society’s digital infrastructure. Many of our digital services and products either leverage or entirely rely on FOSS somewhere in their software stack. However, FOSS sustainability is a multifaceted concept, and the impact of its decline on community products is less known. Objective: In this study, we sought to understand how the different aspects of FOSS sustainability impact software quality from a developmental trajectory perspective. Specifically, we aim to investigate whether and how incubation of FOSS projects or bypassing incubation correlates with software quality outcomes. Method: We selected 342 FOSS projects from the Apache Software Foundation that have either graduated, retired, or bypassed their incubator program (development trajectory phases specific for Apache FOSS projects). We used 16 sustainability metrics to examine their impact on eight software quality metrics (defect density, code coverage, medium risk complexity, very high risk complexity, number of large files, very large function size count, code duplication percentage, and most complex function LOC). We analyzed the decline in the sustainability metrics and their relationship with our selected software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts. Results: We found that our selected sustainability metrics exhibit distinct relationships with software quality across different project trajectories. Graduated projects demonstrated the strongest sustainability-software quality (SWQ) relationship. In contrast, retired projects exhibited weaker relationships, despite receiving similar governance support during incubation. Bypassed projects, while not outperforming graduated ones, showed comparable sustainability-SWQ relationships. Conclusion: While structured incubation strengthens sustainability and SWQ in graduated projects, retired projects struggle to maintain strong sustainability-SWQ relationships, indicating that additional factors internal and specific to projects influence sustainability. This effect was evident among bypassed projects; their self-reliant sustainability practices yielded stronger sustainability-software quality (SWQ) compared to the retired ones.

Code review is a socio-technical practice, yet how software engineers engage in Large Language Model (LLM)-assisted code reviews compared to human peer-led reviews is less understood. We report a two-phase qualitative study with 20 software engineers to understand this. In Phase I, participants exchanged peer reviews and were interviewed about their affective responses and engagement decisions. In Phase II, we introduced a new prompt matching engineers' preferences and probed how characteristics shaped their reactions. We develop an integrative account linking emotional self-regulation to behavioral engagement and resolution. We identify self-regulation strategies that engineers use to regulate their emotions in response to negative feedback: reframing, dialogic regulation, avoidance, and defensiveness. Engagement proceeds through social calibration; engineers align their responses and behaviors to the relational climate and team norms. Trajectories to resolution, in the case of peer-led review, vary by locus (solo/dyad/team) and an internal sense-making process. With the LLM-assisted review, emotional costs and the need for self-regulation seem lower. When LLM feedback aligned with engineers' cognitive expectations, participants reported reduced processing effort and a potentially higher tendency to adopt. We show that LLM-assisted review redirects engagement from emotion management to cognitive load management. We contribute an integrative model of engagement that links emotional self-regulation to behavioral engagement and resolution, showing how affective and cognitive processes influence feedback adoption in peer-led and LLM-assisted code reviews. We conclude that AI is best positioned as a supportive partner to reduce cognitive and emotional load while preserving human accountability and the social meaning of peer review and similar socio-technical activities.

Software development organizations (SDOs) are increasingly working to adopt artificial intelligence (AI) tools, like GitHub Copilot, to meet varied expectations. Nevertheless, we know little about how SDOs manage these expectations. This paper investigates how different SDOs expect AI tools to impact software development, and how these expectations change after a period of considering and evaluating AI tools. We conducted a multiple-case study involving three SDOs. To elicit initial expectations towards AI tools, we collected data using semi-structured interviews and field visits. To assess the persistence of expectations towards AI tools, we collected data from meetings, a debriefing, and retrospectives on AI tools. We found three expectations particular to one SDO; four shared between two SDOs; and six pervasive across all SDOs. Five expectations did not persist after experiential learning with AI tools, due to platform- and SDO-related factors. SDOs must carefully manage their expectations towards AI tools due to the variety and complexity of expectations. Some expectations are niche-specific based on their compatibility with the unique SDOs' people- and structure-related aspects, while others are becoming mainstream for a broader array of SDOs. Recognizing factors that affect the persistence of expectations and how they manifest in the individual SDO will enable SDOs to form their initial expectations and understand how these might change during adoption of AI tools, supporting expectation management.

Psychological safety continues to pique the interest of scholars in a variety of disciplines of study. Recent research indicates that psychological safety fosters knowledge sharing and norm clarity and complements agile values. Although software quality remains a concern in the software industry, academics have yet to investigate whether and how psychologically safe teams provide superior results. In this study, we explore how psychological safety influences agile teams’ quality-related behaviors aimed at enhancing software quality. To widen the empirical coverage and evaluate the results, we chose a two-phase mixed-methods research design with an exploratory qualitative phase (20 interviews) followed by a quantitative phase (survey study, N = 423). Our findings show that, when psychological safety is established in agile software teams, it induces enablers of a social nature that advance the teams’ ability to pursue software quality. For example, admitting mistakes and taking initiatives equally help teams learn and invest their learning in their future decisions related to software quality. Past mistakes become points of reference for avoiding them in the future. Individuals become more willing to take initiatives aimed at enhancing quality practices and mitigating software quality issues. We contribute to our endeavor to understand the circumstances that promote software quality. Psychological safety requires organizations, their management, agile teams, and individuals to maintain and propagate safety principles. Our results also suggest that technological tools and procedures can be utilized alongside social strategies to promote software quality.

Context: Free and Open Source Software (FOSS) communities’ ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically. Objective: This study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality. Method: 16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts. Results: Findings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted. Conclusion: Findings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.