Research
Last update: September 2026
My work connects artificial intelligence (AI), software engineering, and computer‑supported cooperative work (CSCW).
Current research themes
01Human-AI collaboration in software engineering
Effective AI tools should strengthen human judgment, learning, and collaboration—not merely automate tasks.
Selected Findings
- Output quality, practical value, and goal alignment are associated with developers’ trust in GenAI [TOSEM’26].
- Project-grounded agents nearly match humans at answering developer questions, but need briefer responses [ICSE’26].
- Developer-AI tools need varied interaction styles and support for user control [FORGE’25, ICSE’25].
- Workflow-integrated conversational support helps ML end-user programmers [ICSE’24].
- Code-review bots increase merges but reduce discussion; automation creates tradeoffs [EMSE’23, EMSE’22].
- Chatbot language and social characteristics shape user experience [TOCHI, IJHCI].
02Supporting Open Source Software (OSS) communities
Sustainable OSS communities depend on participation, supportive social practices, and a genuine sense of belonging.
Selected Findings
- Identical OSS role titles can hide different duties; explicit responsibilities clarify governance [CHASE’26].
- Mentors, clear onboarding, and suitable tasks support newcomers’ participation [CHASE’25, IST’24].
- Contributor retention requires timely, context-specific support [TOSEM’25, EMSE’22].
- Interpersonal barriers undermine belonging, especially for marginalized contributors [ICSE’25].
- Social connection and intrinsic motivation strengthen belonging in OSS [ICSE’23].
More findings on OSS communities
- Social support and tool redesign can reduce barriers for women in open source software projects [TOSEM’22, ICSE SEIS’22, TSE’20].
- Motivations evolve, and OSS success extends beyond code contributions [ICSE’21, TSE’21].
- Barrier-aware onboarding and task recommendations help newcomers get started [EMSE’23, ICSE’16, CSCW’15].
03Computer science and software engineering education
Effective computing education combines evidence-based pedagogy, authentic software projects, structured guidance, and deliberate support for learning with AI.
Selected Findings
- Novices treated GenAI’s responses as fixed, rarely refining prompts when explanations fell short [CUI’25].
- GenAI supports learning, but guidance is needed to address overreliance [CSEE&T’25, ICSE’24].
- Gamified scaffolding helps students engage with OSS contributions [CSEE&T’25, VL/HCC’24].
- Authentic OSS projects develop skills but need structured guidance [FSE’25, CSEE&T’17].
- Instructors want AI tutors that adapt step-by-step guidance to students’ needs [CONVERSATIONS’23].
- Programming instructors need pedagogical knowledge beyond technical content [ITiCSE’19, FIE’19].
- Students and instructors perceive programming challenges differently [IEEE LATAM’17].
Research approach
- Pragmatism Methods tailored to each problem and context, combining quantitative and qualitative approaches when useful.
- Engineering Real-world problems addressed through purpose-built tools and applications.
- Innovation New ideas and alternative ways of framing problems.
- Quality Rigor and quality throughout the research process.
- Interdisciplinarity Knowledge across domains connected to uncover new problems and opportunities.
Earlier research projects
- FLOSSCoach: a portal to help newcomers overcome barriers to contributing to open source software
- CHOReOS/BAILE: large-scale cloud-service choreography and change-impact analysis with industry partners
- MyFoodScanner: a research-based application using collective intelligence to support informed food choices
- Groupware Workbench: A component-based product line for developing collaborative systems
- Arquigrafia: a social network for architecture and urbanism
- Smart Audio City Guide: a mobile application for blind people
- MetricMiner: a web-based tool for mining software repositories
- AulaNet: computer supported collaborative learning
View selected publications