Zixuan Feng

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.

Context: The success and sustainability of open source software (OSS) projects depend not only on visible technical contributions but also on glue work, the often invisible and underappreciated efforts that hold projects and communities together. Despite their importance, glue work remains inconsistently defined, difficult to trace, and rarely acknowledged. Objectives: This study aims to develop an empirically grounded and theoretically informed understanding of glue work in OSS: what it is, where it appears, and how it can be traced and recognized. Methods: We conducted a multiple-case study across diverse OSS contexts using interviews, focus groups, and surveys. Drawing on invisible labor theory, we developed a taxonomy of glue work structured along sociocultural (what it is), sociospatial (where it appears), and sociolegal (how it is acknowledged) dimensions. We operationalized the taxonomy through glue work Tracker, a modular, extensible, multi-agent GitHub bot prototype, and conducted an expert evaluation with OSS practitioners. Results: The taxonomy identifies four categories and 12 types of sustaining OSS work and specifies how such contributions can be traced and recognized across development and community channels. The expert evaluation shows that the taxonomy and prototype align with real recognition needs and are feasible to integrate into project workflows. Conclusion: This work provides both a theoretical foundation and a practical infrastructure for making sustaining labor visible and supporting OSS sustainability.

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.

Machine Learning (ML) is increasingly gaining significance for enduser programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named "Newton" as an expert to support ML-EUPs. Newton's design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton's design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities.

Maintaining and retaining contributors is crucial for Open Source (OSS) projects. However, there is often a high turnover among contributors (in some projects as high as 80%). The survivability of contributors is influenced by various factors, including their demographics. Research on contributors’ survivability must, therefore, consider diversity factors. This study longitudinally analyzed the impact of demographic attributes on survivability in the Flutter community through the lens of gender, region, and compensation. The preliminary analysis reveals that affiliated or Western contributors have a higher survival probability than volunteer or Non-Western contributors. However, no significant difference was found in the survival probability between men and women.