PhD Student at Carnegie Mellon University
PhD Student at University of LisbonI am a PhD student working under the supervision of Alcides Fonseca, Sara Silva, and Christopher S. Timperley. My research focuses on empirically studying bugs and developing program analysis techniques to detect errors in software systems. I have worked on code generation using evolutionary computation, and I am closely researching the application of AI, programming languages, and software engineering techniques to robot software. Overall, I am interested in Program Analysis, Applied AI, and Code Generation & Repair.
I am currently on the industry job market! 💼
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Carnegie Mellon UniversitySep. 2020 - Jul. 2026 (Expected)Dual Degree PhD in Software Engineering with University of LisbonThesis: Specification-Driven Detection of Misconfigurations in ROS-based Robotic SystemsAdvisor: Alcides Fonseca, Sara Silva, and Chris Timperley
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Faculty of Sciences of University of LisbonSep. 2018 - Jul. 2020MSc. in Computer ScienceThesis: Towards the Conceptualization of Refinement Typed Genetic ProgrammingAdvisor: Alcides Fonseca
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Faculty of Sciences of University of LisbonSep. 2015 - Jul. 2018BSc. in Computer Science
Work Experience
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Uber Technologies Inc.May 2024 - Aug. 2024PhD Software Engineer Research Intern, mentored by Stefan Heule and Yuxin Wang in the Programming Systems Group.
Teaching Experience
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Carnegie Mellon UniversityTeaching Assistant17-643 - Quality ManagementMar. 2024 - May 202417-623 - Quality AssuranceOct. 2023 - Dec. 2023
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Faculty of Sciences of University of LisbonInvited Teaching AssistantProgrammingSep. 2021 - Feb. 2022Object Oriented DevelopmentJan. 2021 - Jun. 2021
Selected Publications
LGTM! Characteristics of Auto-Merged LLM-based Agentic PRs
Ruben Branco*, Paulo Canelas*, Catarina Gamboa*, Alcides Fonseca (* equal contribution)
Mining Challenge at International Conference on Mining Software Repositories (MSR). 2026. Just Accepted! 🎉
AI tools are generating code faster than humans can properly review it, leading repositories to skip review and auto-merge agentic Pull Requests (PR) directly. In this study, we analyze the characteristics of auto-merged agentic PRs and compare them to human-authored ones. We examine code characteristics, repository ecosystems, and agentic tools across the AIDev dataset, spanning diverse software engineering tasks. We find that auto-merged PRs are smaller and more focused, and that repositories tend to either auto-merge all or none agentic PRs, with more mature repositories favoring the latter. Compared to human-authored auto-merges, maintainers auto-merge agentic PRs more often but show caution toward PRs that delete existing code. Among agents, OpenAI Codex and Claude Code receive the highest auto-merge rates. These findings can inform agentic tool design and repository's auto-merge decisions.
ROSpec: A Domain-Specific Language for ROS-based Robot Software
Paulo Canelas, Bradley Schmerl, Alcides Fonseca, Christopher S. Timperley
Proceedings of the ACM on Programming Languages (PACMPL), OOPSLA. 2025.
Are Large Language Models Memorizing Bug Benchmarks?
Daniel Ramos, Claudia Mamede*, Kush Jain*, Paulo Canelas*, Catarina Gamboa*, Claire Le Goues (* equal contribution)
Large Language Models for Code (LLM4Code) Workshop. 2025. 🏆 Best Paper Award.