arXiv subjects

Bianca Trinkenreich

Publications and source records attributed to Bianca Trinkenreich.

At least 19 recordsLinked to original sources

arXiv2026

An Empirical Study of the TianoCore Community

We investigate the software security and maintenance practices adopted by stakeholders in the TianoCore community and identify opportunities to improve firmware development workflows. We conduct a survey and a limited interview study with participants representing independent firmware vendors, original equipment manufacturers, security experts, firmware developers, and academic researchers. This open-source development community maintains a reference implementation for the core of the UEFI firmware. We highlight important gaps in the current state of firmware development within the TianoCore ecosystem and identify key areas in which improved security practices, greater adoption of memory-safe technologies, and increased automation of manual processes could strengthen the maintenance and security of the UEFI firmware.

cs.SE↗

arXiv2026

On the Influence of Refactoring Types on Merge Effort

Modern software development involves parallel work and concurrent changes, requiring code merging. Prior studies report that 10% to 20% of merge attempts result in conflicts, often requiring manual intervention. The literature explores factors that generate conflicts, including refactorings, but does not analyze how individual refactoring types influence the manual effort required to resolve them. We analyzed 64 open-source Java projects and applied association rule mining to measure the strength of associations between specific refactoring types and merge effort. Our results show that refactoring types relate to merge effort with varying strength. In particular, Rename Attribute, Move Class, Extract Variable, Change Return Type, and Split Parameter exhibit some of the strongest associations, especially when a higher number of such refactorings is present in the merge branches. We also find that both the number of refactorings and their diversity independently increase merge effort, both in terms of occurrence and intensity. Additionally, the co-occurrence of refactorings across parallel branches is associated with higher merge effort, particularly when combining structural transformations with changes to method signatures and data-structure representations, whereas more localized changes are less frequent in the most impactful combinations.

cs.SE↗

arXiv2026

Making AI Visible, Not Vanished: How AI Policies Reshape Developer Experience on GitHub

Generative AI is rapidly reshaping Open Source Software (OSS) software development,prompting projects to introduce policies governing AI-assisted contributions. However, little is known about how these policies differ or whether they influence developer experience. We present the first large-scale empirical study of AI governance policies in OSS. Analyzing 29,624 GitHub repositories, we identify 385 projects that adopted AI policies and derive TRACE, a framework capturing five governance dimensions: Transparency, Responsibility, Attribution, Constraints, and Enforcement. We further classify policies into five governance families and estimate their effects using propensity-score matching and longitudinal difference-in-differences analysis. Our results show that AI governance primarily regulates rather than prohibits AI-assisted development. Policy adoption brings maintainer engagement, increased AI disclosure, richer review interactions, and improved code quality while AI-assisted contributions continue to grow. Governance design matters: policies emphasizing transparency and responsibility produced stronger community and quality outcomes than restrictive approaches alone. Our findings show how different AI governance strategies shape developer experience and provide evidence to help OSS communities design effective AI policies.

cs.SE↗

arXiv2026

Guidelines for Cultivating a Sense of Belonging to Reduce Developer Burnout

Burnout affects software developers' mental and physical well-being and contributes to turnover, generating strong concerns in the software industry. Prior research has shown that lack of belonging is associated with higher levels of burnout among software developers, while a sense of belonging is linked to resilience, job satisfaction, engagement, and well-being. In this paper, we revisit recent studies on belongingness in software development teams, including proprietary software organizations and open-source software communities, to offer evidence-based guidelines for cultivating belongingness and reducing developer burnout. We summarize characteristics of belongingness, such as trust, acceptance, value recognition, friendship, membership, mutual support, and being known by others, as well as factors associated with belongingness, including recognition, psychological safety, intrinsic motivation, English confidence, tenure, gender, and cultural power distance. Based on these findings, we propose practical guidelines for leaders and communities, including timely and consistent recognition, transparent promotion rules, inclusive benefits and initiatives, intentional connections through collaborative tools, blameless postmortems, optional in-person opportunities, informal newcomer gatherings, and continuous monitoring of belongingness and burnout. These guidelines can help software organizations and open-source communities foster healthier, more inclusive environments that support developer well-being.

cs.SE↗

arXiv2026

Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape

Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices. Despite rapid adoption, there is limited empirical evidence on how GenAI is used in SE research and its implications for research practices and governance. Aims: We conduct a large-scale survey of 457 SE researchers publishing in top venues between 2023 and 2025. Method: Using quantitative and qualitative analyses, we examine who uses GenAI and why, where it is used across research activities, and how researchers perceive its benefits, opportunities, challenges, risks, and governance. Results: GenAI use is widespread, with many researchers reporting pressure to adopt and align their work with it. Usage is concentrated in writing and early-stage activities, while methodological and analytical tasks remain largely human-driven. Although productivity gains are widely perceived, concerns about trust, correctness, and regulatory uncertainty persist. Researchers highlight risks such as inaccuracies and bias, emphasize mitigation through human oversight and verification, and call for clearer governance, including guidance on responsible use and peer review. Conclusion: We provide a fine-grained, SE-specific characterization of GenAI use across research activities, along with taxonomies of GenAI use cases for research and peer review, opportunities, risks, mitigation strategies, and governance needs. These findings establish an empirical baseline for the responsible integration of GenAI into academic practice.

cs.SE↗

arXiv2026

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↗

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

The Role of Social Identity in Shaping Biases Against Minorities in Software Organizations

While systemic workplace bias is well-documented in non-computing fields, its specific impact on software engineers remains poorly understood. This study addresses that gap by applying Social Identity Theory (SIT) to investigate four distinct forms of bias: lack of career development, stereotyped task selection, unwelcoming environments, and identity attacks. Using a vignette-based survey, we quantified the prevalence of these biases, identified the demographics most affected, assessed their consequences, and explored the motivations behind biased actions. Our results show that career development and task selection biases are the most prevalent forms, with over two-thirds of victims experiencing them multiple times. Women were more than three times as likely as men to face career development bias, task selection bias, and an unwelcoming environment. In parallel, individuals from marginalized ethnic backgrounds were disproportionately targeted by identity attacks. Our analysis also confirms that, beyond gender and race, factors such as age, years of experience, organization size, and geographic location are significant predictors of bias victimization.

cs.SE↗

arXiv2025

LLM-Assisted Thematic Analysis: Opportunities, Limitations, and Recommendations

[Context] Large Language Models (LLMs) are increasingly used to assist qualitative research in Software Engineering (SE), yet the methodological implications of this usage remain underexplored. Their integration into interpretive processes such as thematic analysis raises fundamental questions about rigor, transparency, and researcher agency. [Objective] This study investigates how experienced SE researchers conceptualize the opportunities, risks, and methodological implications of integrating LLMs into thematic analysis. [Method] A reflective workshop with 25 ISERN researchers guided participants through structured discussions of LLM-assisted open coding, theme generation, and theme reviewing, using color-coded canvases to document perceived opportunities, limitations, and recommendations. [Results] Participants recognized potential efficiency and scalability gains, but highlighted risks related to bias, contextual loss, reproducibility, and the rapid evolution of LLMs. They also emphasized the need for prompting literacy and continuous human oversight. [Conclusion] Findings portray LLMs as tools that can support, but not substitute, interpretive analysis. The study contributes to ongoing community reflections on how LLMs can responsibly enhance qualitative research in SE.

cs.SE↗

arXiv2025

What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI

Generative AI (genAI) tools promise productivity gains, yet miscalibrated trust and usage friction still hinder adoption. Moreover, genAI can be exclusionary, failing to adequately support diverse users. One such aspect of diversity is cognitive diversity, which leads to diverging interaction styles (e.g., a risk-averse developer may gate genAI outputs behind tests/review; a risk-tolerant one may prototype directly/fix issues post-hoc). When an individual's cognitive styles are unsupported, it creates additional usability barriers. Thus, to design tools that developers trust and use, we must first understand which factors shape their trust and intentions to use genAI at work? We developed a theoretical model of developers' trust and adoption of genAI through a large-scale survey (N = 238) conducted at GitHub and Microsoft. Using Partial Least Squares-Structural Equation Modeling (PLS-SEM), we found aspects related to genAI's system/output quality (e.g., presentation, safety/security, performance), functional value (e.g., educational/practical benefits), and goal maintenance (ability to sustain alignment with task goals) significantly influence trust, which, alongside developers' cognitive styles (i.e., risk tolerance, technophilic motivations, computer self-efficacy), affect adoption. An Importance-Performance Matrix Analysis (IPMA) identified high-importance factors where genAI underperforms, revealing targets for design improvement. We bolster these findings by qualitatively analyzing developers' reported challenges and risks of genAI use to uncover why these gaps persist in development contexts. We offer practical guidance for designing genAI tools that support effective, trustworthy, and inclusive developer-AI interactions.

cs.HC↗

arXiv2025

Investigating the Use of LLMs for Evidence Briefings Generation in Software Engineering

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

cs.SE↗

arXiv2025

Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research

The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.

cs.SE↗

arXiv2025

Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions

Introduction: Digital products increasingly reshape industries, influencing human behavior and decision-making. However, the software development teams developing these systems often lack diversity, which may lead to designs that overlook the needs, equal treatment or safety of diverse user groups. These risks highlight the need for fostering diversity and inclusion in software development to create safer, more equitable technology. Method: This research is based on insights from an academic meeting in June 2023 involving 23 software engineering researchers and practitioners. We used the collaborative discussion method 1-2-4-ALL as a systematic research approach and identified six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI). We identified benefits, harms, and future research directions for the four main themes. Then, we discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes. Results: This research explores the key challenges and research opportunities for promoting SDDI, providing a roadmap to guide both researchers and practitioners. We underline that research around SDDI requires a constant focus on maximizing benefits while minimizing harms, especially to vulnerable groups. As a research community, we must strike this balance in a responsible way.

cs.SE↗

arXiv2025

Impostor Phenomenon Among Software Engineers: Investigating Gender Differences and Well-Being

Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.

cs.SE↗

arXiv2024

Insights from the Frontline: GenAI Utilization Among Software Engineering Students

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.

cs.HC↗

arXiv2024

What Guides Our Choices? Modeling Developers' Trust and Behavioral Intentions Towards GenAI

Generative AI (genAI) tools, such as ChatGPT or Copilot, are advertised to improve developer productivity and are being integrated into software development. However, misaligned trust, skepticism, and usability concerns can impede the adoption of such tools. Research also indicates that AI can be exclusionary, failing to support diverse users adequately. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in perspectives and interaction styles. When an individual's cognitive style is unsupported, it creates barriers to technology adoption. Therefore, to understand how to effectively integrate genAI tools into software development, it is first important to model what factors affect developers' trust and intentions to adopt genAI tools in practice? We developed a theoretically grounded statistical model to (1) identify factors that influence developers' trust in genAI tools and (2) examine the relationship between developers' trust, cognitive styles, and their intentions to use these tools in their work. We surveyed software developers (N=238) at two major global tech organizations: GitHub Inc. and Microsoft; and employed Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate our model. Our findings reveal that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust in these tools. Furthermore, developers' trust and cognitive styles influence their intentions to use these tools in their work. We offer practical suggestions for designing genAI tools for effective use and inclusive user experience.

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

Can AI Serve as a Substitute for Human Subjects in Software Engineering Research?

Research within sociotechnical domains, such as Software Engineering, fundamentally requires a thorough consideration of the human perspective. However, traditional qualitative data collection methods suffer from challenges related to scale, labor intensity, and the increasing difficulty of participant recruitment. This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI), especially large language models (LLMs) like ChatGPT. We explore the potential of AI-generated synthetic text as an alternative source of qualitative data, by discussing how LLMs can replicate human responses and behaviors in research settings. We examine the application of AI in automating data collection across various methodologies, including persona-based prompting for interviews, multi-persona dialogue for focus groups, and mega-persona responses for surveys. Additionally, we discuss the prospective development of new foundation models aimed at emulating human behavior in observational studies and user evaluations. By simulating human interaction and feedback, these AI models could offer scalable and efficient means of data generation, while providing insights into human attitudes, experiences, and performance. We discuss several open problems and research opportunities to implement this vision and conclude that while AI could augment aspects of data gathering in software engineering research, it cannot replace the nuanced, empathetic understanding inherent in human subjects in some cases, and an integrated approach where both AI and human-generated data coexist will likely yield the most effective outcomes.

cs.SE↗