Mairieli Wessel

Ronnie de Souza Santos, Italo Santos, Giuseppe Destefanis et al.

Academic integrity in higher education is increasingly shaped by complex socio-technical environments marked by automated tools, evolving institutional practices, and heightened performance pressures. Within this context, large language models (LLMs) are becoming prevalent in software engineering education, further blurring boundaries around acceptable assistance and authorship. This study investigates how software engineering students describe their emotional experiences after using LLMs in ways they perceive as academically inappropriate. We conducted a cross-sectional survey with 116 undergraduate students. Results show emotionally heterogeneous responses. Indifference was most frequent, including among students who recognized risks to learning and academic standing. Guilt and anxiety were reported in relation to moral discomfort and concern about penalties. Relief and satisfaction were evident primarily in deadline-driven contexts and situations of unclear guidance.

Pien Rooijendijk, Christoph Treude, Mairieli Wessel

Developers may reference vulnerabilities in pull request discussions through both explicit identifiers, such as CVEs or GHSAs, and implicit security-related language (e.g., "unauthorized access" or "SQL injection"). Prior work has primarily focused on explicit identifiers, potentially overlooking vulnerability discussions that lack formal references. Bots and coding agents are becoming more common in pull requests, raising new questions about how different accounts communicate about vulnerabilities. In this registered report, we describe our planned study of vulnerability communication in pull requests by humans, bots, and coding agents. Building on the AIDev-pop dataset, we analyze explicit vulnerability references and implicit security-related signals across pull request titles, descriptions, review comments, commit messages, and timeline discussions. We further investigate whether these references are associated with vulnerabilities introduced or fixed in the modified code and how they relate to pull request review activity and outcomes. This study contributes a large-scale empirical investigation of vulnerability communication practices in modern software development.

Jonan Richards, Bruno Alves de Oliveira, Iury Oliveira et al.

Conversational LLM-based ``programming assistants'' provide a range of benefits to developers. However, recent studies demonstrate the variety in individual developers' needs regarding programming assistants, and challenges encountered by only specific groups of developers. In this study, we explore the role of cognitive diversity in shaping interactions with GitHub Copilot chat. Through a mixed-methods think aloud study with 27 professional developers and students, we characterize 5 distinct ``interaction modes'' and 10 underlying needs in developers' interactions, forming a conceptual model. We characterize links between these modes, needs, and developers' problem-solving styles and experience profiles, showing how cognitive diversity may shape developers' interactions. We provide insights and recommendations for researchers and practitioners on how to design, research, and employ programming assistants to better account for diverse developer needs.