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 developing program analysis techniques to detect errors in software systems. Previously, I worked on evolutionary program synthesis using refinement types, and I am currently closely researching the application of software engineering techniques to the robotics field (Software Engineering for Robotics).
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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
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
Are Large Language Models Memorizing Bug Benchmarks?
Daniel Ramos, Claudia Mamede*, Kush Jain*, Paulo Canelas*, Catarina Gamboa*, Claire Le Goues (* equal contribution)
ArXiv. Under review. 2024.
Large Language Models (LLMs) have become integral to various software engineering tasks, including code generation, bug detection, and repair. To evaluate model performance in these domains, numerous bug benchmarks containing real-world bugs from software projects have been developed. However, a growing concern within the software engineering community is that these benchmarks may not reliably reflect true LLM performance due to the risk of data leakage. Despite this concern, limited research has been conducted to quantify the impact of potential leakage. In this paper, we systematically evaluate popular LLMs to assess their susceptibility to data leakage from widely used bug benchmarks. To identify potential leakage, we use multiple metrics, including a study of benchmark membership within commonly used training datasets, as well as analyses of negative log-likelihood and *n*-gram accuracy. Our findings show that certain models, in particular CodeGen, exhibit significant evidence of memorization in widely used benchmarks like Defects4J, while newer models trained on larger datasets like Llama 3.1 exhibit limited signs of leakage. These results highlight the need for careful benchmark selection and the adoption of robust metrics to adequately assess models capabilities.
Understanding Misconfigurations in ROS: An Empirical Study and Current Approaches
Paulo Canelas, Bradley Schmerl, Alcides Fonseca, Christopher S. Timperley
International Symposium on Software Testing and Analysis (ISSTA). 2024.
The Robot Operating System (ROS) is a popular framework for building robot software from reusable components, but configuring and connecting these components correctly is challenging. Developers often face issues due to unstated assumptions, leading to misconfigurations that can result in unpredictable and dangerous behavior. To improve the reliability of ROS projects, it is critical to identify the broader set of misconfigurations. To that end, we perform a study on ROS Answers, a Q&A platform, to categorize these misconfigurations and evaluate how well existing detection techniques cover them. We identified 12 high-level categories and 50 sub-categories, with 27 not covered by current techniques.