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
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.
Is it a Bug? Understanding Physical Unit Mismatches in Robot Software
Paulo Canelas, Trenton Tabor, John-Paul Ore, Alcides Fonseca, Claire Le Goues, Christopher S. Timperley
International Conference in Robotics and Automation (ICRA). 2024.
Robot software often involves variables representing physical units like meters and seconds. Incorrect operations on these units can cause dangerous system behaviors, and detecting such errors manually is challenging. Current analysis techniques use dimensional analysis rules to find mismatches but often flag intentional mismatches, leading to false positives that impede robotics developer trust and productivity. This study inspects 180 errors detected by the Phys tool and identifies three types of physical unit mismatches, categorizing them into eight high-level categories. We find that developers often introduce intentional mismatches, such as differential drives, small angle approximations, and controls.