arXiv subjects

Zixuan Feng

Publications and source records attributed to Zixuan Feng.

At least 19 recordsLinked to original sources

arXiv2026

''You Can't Open an LLM With a Screwdriver'': The De-Democratization of Software

Claims that generative AI will soon write all of the code have led to predictions that programming is nearing its end. In this vision paper, we argue against this assumption that broader access to code generation necessarily democratizes software development, i.e., everyone can code but we have to distinguish between access and control: by access, we mean the ability of more people, including non-experts and less-experienced developers, to generate code-like artifacts with AI; by control, we mean the capacity to inspect, evaluate, integrate, maintain, and govern those artifacts as dependable software. While AI may broaden access to code production, control may become more concentrated among those who own or understand the code, software practices, infrastructure, evaluation practices, and deployment pipelines. Grounded in an expert panel, our vision paper argues that AI does not eliminate software engineering expertise but shifts where that expertise becomes most critical. The locus of software engineering expertise is shifting toward intent specification: orchestrating and governing AI behavior, evaluating software behavior, and integrating software systems. We conclude this paper by identifying research opportunities for education, tools, and policy that can help the software engineering community respond to the AI era with greater agency, accountability, and adaptability.

cs.SE↗

arXiv2026

"AI Slop is DDoSing Open Source": Understanding the Impact of AI-Generated Contributions on Open Source Sustainability

Open source software (OSS) communities are facing increasing pressure from Generative AI (GenAI) tools. We call it AI-DDoS: a denial-of-service effect in which plausible but low-quality AI-generated contributions overwhelm OSS community capacity. Using a phenomenon-based mixed-methods approach, we first analyze practitioner accounts from Reddit, OSS mentor mailing lists, and blogs to identify six recurring themes and derive hypotheses. We then evaluate these hypotheses using Bayesian Structural Time Series analysis across 294 repositories with over 2 million pull requests and issues. Our results show that while PR volume increased in 2025, merge rates declined, with one-time contributors experiencing an 18.18% drop in PR merge rates relative to the counterfactual. Finally, we identify 11 remediation strategies through practitioners' interviews and validate them with a survey of 229 OSS practitioners, grouping them into preservative, adaptive, and transformative orientations. Our findings show that AI-DDoS is not only a contribution-volume problem but a sustainability trap: communities often default to low-effort defensive strategies that protect short-term review capacity while making openness difficult to sustain.

cs.SE↗

arXiv2026

Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload

Onboarding documentation is critical for attracting and retaining newcomers in open source software (OSS). However, it is often presented as dense, inconsistently structured, and fragmented presentations that are difficult to understand, which creates cognitive overload leading to frustration, errors, and abandonment. Here, we investigate how Cognitive Theory of Multimedia Learning (CTML) strategies can be used to restructure OSS documentation. We use a GenAI-based pipeline to operationalize these strategies to restructure OSS documentation through our prototype VisDoc. VisDoc segments documentation into task-based units, infers workflows, removes redundancy, and generates multimodal explanations. An expert evaluation (N=4) affirmed VisDoc's completeness, accuracy, and adoptability; A between-subjects evaluation (N=14) with newcomers found that VisDoc participants achieved higher task success, had significantly lower cognitive load, and perceived higher usability. The contributions of this work include a CTML-grounded analysis of onboarding challenges, a GenAI-based documentation restructuring pipeline, and empirical evidence that cognitively informed documentation restructuring reduces cognitive load and improves usability and task performance in OSS.

cs.SE↗

arXiv2026

Addressing OSS Community Managers' Challenges in Contributor Retention

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.

cs.SE↗

arXiv2026

A Bayesian Multi-State Data Integration Approach for Estimating County-level Prevalence of Opioid Misuse in the United States

Drug overdose deaths, including from opioids, remain a significant public health threat to the United States (US). To abate the harms of opioid misuse, understanding its prevalence at the local level is crucial for stakeholders in communities to develop response strategies that effectively use limited resources. Although there exist several state-specific studies that provide county-level prevalence estimates, such estimates are not widely available across the country, as the datasets used in these studies are not always readily available in other states, which, therefore, has limited the wider applications of existing models. To fill this gap, we propose a Bayesian multi-state data integration approach that fully utilizes publicly available data sources to estimate county-level opioid misuse prevalence for all counties in the US. The hierarchical structure jointly models opioid misuse prevalence and overdose death outcomes, leverages existing county-level prevalence estimates in limited states and state-level estimates from national surveys, and accounts for heterogeneity across counties and states with counties' covariates and mixed effects. Furthermore, our parsimonious and generalizable modeling framework employs horseshoe+ prior to flexibly shrink coefficients and prevent overfitting, ensuring adaptability as new county-level prevalence data in additional states become available. Using real-world data, our model shows high estimation accuracy through cross-validation and provides nationwide county-level estimates of opioid misuse for the first time.

stat.AP↗

arXiv2025

The Fast and Spurious: Developer Productivity with GenAI

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.

cs.SE↗

arXiv2025

From Gains to Strains: Modeling Developer Burnout with GenAI Adoption

Generative AI (GenAI) is rapidly reshaping software development workflows. While prior studies emphasize productivity gains, the adoption of GenAI also introduces new pressures that may harm developers' well-being. In this paper, we investigate the relationship between the adoption of GenAI and developers' burnout. We utilized the Job Demands--Resources (JD--R) model as the analytic lens in our empirical study. We employed a concurrent embedded mixed-methods research design, integrating quantitative and qualitative evidence. We first surveyed 442 developers across diverse organizations, roles, and levels of experience. We then employed Partial Least Squares--Structural Equation Modeling (PLS-SEM) and regression to model the relationships among job demands, job resources, and burnout, complemented by a qualitative analysis of open-ended responses to contextualize the quantitative findings. Our results show that GenAI adoption heightens burnout by increasing job demands, while job resources and positive perceptions of GenAI mitigate these effects, reframing adoption as an opportunity.

cs.SE↗

arXiv2025

Charting Uncertain Waters: A Socio-Technical Roadmap for Sustaining Open Source Communities in the Age of GenAI

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.

cs.SE↗

arXiv2025

When Domains Collide: An Activity Theory Exploration of Cross-Disciplinary Collaboration

Background: Software development teams are increasingly diverse, embedded, and cross-disciplinary. Domain experts (DEs) from different disciplines collaborate with professional software developers (SDEs), bringing complementary expertise in creating and maintaining complex production software. However, contested expectations, divergent problem-solving perspectives, and conflicting priorities lead to friction. Aims: This study aims to investigate the dynamics of emerging collaboration of cross-disciplinary software development (CDSD) by exploring the expectations held by DEs and SDEs and understanding how these frictions manifest in practice. Method: We utilize Activity Theory (AT), a well-established socio-technical framework, as an analytical lens in a grounded, empirical investigation, conducted through a mixed-method study involving 24 interviews (12 DEs and 12 SDEs) and a large-scale validation survey with 293 participants (161 DEs and 132 SDEs). Results: We conceptualize and empirically ground the CDSD dynamics. We identified eight expectations held by SDEs and six by DEs. By mapping these expectations to AT components, we revealed 21 frictions in CDSD and illustrated where and how they arise. Conclusions: This study offers a theoretical lens for understanding the dynamics and frictions in CDSD and provides actionable insights for future research, practitioners, and infrastructure design.

cs.SE↗

arXiv2025

Quon Classical Simulation: Unifying Cliffords, Matchgates and Entanglement

We propose a new framework of topological complexity to study the computational complexity of quantum circuits and tensor networks. Within this framework, we establish the Quon Classical Simulation (QCS) for hybrid Clifford-Matchgate circuits, which is efficient for both Clifford circuits and Matchgate circuits, therefore answering a long standing open question on unifying efficient classical simulations. This framework is built upon the Quon language, a 2+1D topological quantum field theory with space-time boundary and defects. Its exponential computation complexity is captured by Magic holes, a topological feature capturing the global long-range entanglement. Both Clifford circuits and Matchgate circuits are free of Magic holes. Efficient classical simulations of Cliffords and Matchgates are implemented by two parallel operations, generalized surgery theory of 3-manifolds and Yang-Baxter relations on the 2D boundary respectively, with additional binary arithmetic properties.

quant-ph↗

arXiv2025

Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models

The organization of latent token representations plays a crucial role in determining the stability, generalization, and contextual consistency of language models, yet conventional approaches to embedding refinement often rely on parameter modifications that introduce additional computational overhead. A hierarchical alignment method was introduced to restructure token embeddings without altering core model weights, ensuring that representational distributions maintained coherence across different linguistic contexts. Experimental evaluations demonstrated improvements in rare token retrieval, adversarial robustness, and long-range dependency tracking, highlighting the advantages of hierarchical structuring in mitigating inconsistencies in latent space organization. The comparative analysis against conventional fine-tuning and embedding perturbation methods revealed that hierarchical restructuring maintained computational efficiency while achieving measurable gains in representation quality. Structural refinements introduced through the alignment process resulted in improved contextual stability across varied linguistic tasks, reducing inconsistencies in token proximity relationships and enhancing interpretability in language generation. A detailed computational assessment confirmed that the realignment process introduced minimal inference overhead, ensuring that representational improvements did not compromise model efficiency. The findings reinforced the broader significance of structured representation learning, illustrating that hierarchical embedding modifications could serve as an effective strategy for refining latent space distributions while preserving pre-learned semantic associations.

cs.CL↗

arXiv2025

The Multifaceted Nature of Mentoring in OSS: Strategies, Qualities, and Ideal Outcomes

Mentorship in open source software (OSS) is a vital, multifaceted process that includes onboarding newcomers, fostering skill development, and enhancing community building. This study examines task-focused mentoring strategies that help mentees complete their tasks and the ideal personal qualities and outcomes of good mentorship in OSS communities. We conducted two surveys to gather contributor perceptions: the first survey, with 70 mentors, mapped 17 mentoring challenges to 21 strategies that help support mentees. The second survey, with 85 contributors, assessed the importance of personal qualities and ideal mentorship outcomes. Our findings not only provide actionable strategies to help mentees overcome challenges and become successful contributors but also guide current and future mentors and OSS communities in understanding the personal qualities that are the cornerstone of good mentorship and the outcomes that mentor-mentee pairs should aspire to achieve.

cs.HC↗

arXiv2024

Investigating the Impact of Interpersonal Challenges on Feeling Welcome in OSS

The sustainability of open source software (OSS) projects hinges on contributor retention. Interpersonal challenges can inhibit a feeling of welcomeness among contributors, particularly from underrepresented groups, which impacts their decision to continue with the project. How much this impact is, varies among individuals, underlining the importance of a thorough understanding of their effects. Here, we investigate the effects of interpersonal challenges on the sense of welcomeness among diverse populations within OSS, through the diversity lenses of gender, race, and (dis)ability. We analyzed the large-scale Linux Foundation Diversity and Inclusion survey (n = 706) to model a theoretical framework linking interpersonal challenges with the sense of welcomeness through Structural Equation Models Partial Least Squares (PLS-SEM). We then examine the model to identify the impact of these challenges on different demographics through Multi-Group Analysis (MGA). Finally, we conducted a regression analysis to investigate how differently people from different demographics experience different types of interpersonal challenges. Our findings confirm the negative association between interpersonal challenges and the feeling of welcomeness in OSS, with this relationship being more pronounced among gender minorities and people with disabilities. We found that different challenges have unique impacts on how people feel welcomed, with variations across gender, race, and disability groups. We also provide evidence that people from gender minorities and with disabilities are more likely to experience interpersonal challenges than their counterparts, especially when we analyze stalking, sexual harassment, and doxxing. Our insights benefit OSS communities, informing potential strategies to improve the landscape of interpersonal relationships, ultimately fostering more inclusive and welcoming communities.

cs.SE↗

arXiv2023

Make It Make Sense! Understanding and Facilitating Sensemaking in Computational Notebooks

Reusing and making sense of other scientists' computational notebooks. However, making sense of existing notebooks is a struggle, as these reference notebooks are often exploratory, have messy structures, include multiple alternatives, and have little explanation. To help mitigate these issues, we developed a catalog of cognitive tasks associated with the sensemaking process. Utilizing this catalog, we introduce Porpoise: an interactive overlay on computational notebooks. Porpoise integrates computational notebook features with digital design, grouping cells into labeled sections that can be expanded, collapsed, or annotated for improved sensemaking. We investigated data scientists' needs with unfamiliar computational notebooks and investigated the impact of Porpoise adaptations on their comprehension process. Our counterbalanced study with 24 data scientists found Porpoise enhanced code comprehension, making the experience more akin to reading a book, with one participant describing it as It's really like reading a book.

cs.HC↗

arXiv2023

Unveiling Diversity: Empowering OSS Project Leaders with Community Diversity and Turnover Dashboards

Managing open-source software (OSS) projects requires managing communities of contributors. In particular, it is essential for project leaders to understand their community's diversity and turnover. We present CommunityTapestry, a dynamic real-time community dashboard, which presents key diversity and turnover signals that we identified from the literature and through participatory design sessions with stakeholders. We evaluated CommunityTapestry with an OSS project's contributors and Project Management Committee members, who explored the dashboard using their own project data. Our study results demonstrate that CommunityTapestry increased participants' awareness of their community composition and the diversity and turnover rates in the project. It helped them identify areas of improvement and gave them actionable information.

cs.SE↗

arXiv2023

Spin Statistics and Field Equations for any Spin

In this article we prove spin statistics theorem for arbitrary massive (A, B) field in a representation theoretic manner. General Gamma matrices are introduced, and explicit forms for low spin are calculated. Spin sums and twisted spin sums are introduced to prove spin statistics and derive field equations respectively, and their relations are discussed. Klein Gordan equation is just the condition on the 4-momentum. Dirac equation (or massive Weyl equations) and Proca equation are shown to be examples of our general field equation.

math-ph↗

arXiv2023

The State of Diversity and Inclusion in Apache: A Pulse Check

Diversity and inclusion in open source software (OSS) is a multifaceted concept that arises from differences in contributors' gender, seniority, language, region, and other characteristics. D&I has received growing attention in OSS ecosystems and projects, and various programs have been implemented to foster contributor diversity. However, we do not yet know how the state of D&I is evolving. By understanding the state of D&I in OSS projects, the community can develop new and adjust current strategies to foster diversity among contributors and gain insights into the mechanisms and processes that facilitate the development of inclusive communities. In this paper, we report and compare the results of two surveys of Apache Software Foundation (ASF) contributors conducted over two years (n=624 & n=432), considering a variety of D&I aspects. We see improvements in engagement among those traditionally underrepresented in OSS, particularly those who are in gender minority or not confident in English. Yet, the gender gap in the number of contributors remains. We expect this study to help communities tailor their efforts in promoting D&I in OSS.

cs.SI↗

arXiv2023

Representation theory in the construction of free quantum field

This is mainly a lecture note taken by myself following Weinberg's book, but also contains some corrections to the abuse of mathematical treatment. This article discusses projective unitary representations of Poincare group on the single particle space, multi particle space also known as the Fock space, creation and annilation operators, construction of free quantum fields and the general relation between spin of state and spin of field. Both massive and massless cases are considered. CPT is not considered. The first section briefly reviews the basics of representation theory. This article further points out some of the wrong treatment of mathematics in the book of Weinberg, and reformulates them, including: Wigner's classification needs to be pass to the universal cover via Bargmann's theorem, there is no projective representation of Poincare group on Fock space in general, the Lorentz transformation of fields need to be formulated with representations of the universal covers, Dirac representation is not a linear representation of the Lorentz group. This article also discusses the physical meaning of the state representation and its relation with Schrodinger equation, compare its difference with state representation, and the reason of equations of relativistic quantum mechanics should be understood as a field equation rather than a wave function equation. of equations of relativistic quantum mechanics should be understood as a field equation rather than a wave function equation.

math-ph↗