2024
- Generative AI to Generate Test Data Generators (Benoit Baudry, Khashayar Etemadi, Sen Fang, Yogya Gamage, Yi Liu, Yuxin Liu, Martin Monperrus, Javier Ron, André Silva and Deepika Tiwari), In IEEE Software, 2024.
- ITER: Iterative Neural Repair for Multi-Location Patches (He Ye and Martin Monperrus), In Proceedings of International Conference on Software Engineering, 2024.
- Scoping Software Engineering for AI: The TSE Perspective (Sebastian Uchitel, Martin Monperrus and Hao Zhong), In IEEE Transactions on Software Engineering, Institute of Electrical and Electronics Engineers (IEEE), volume 50, 2024.
- Supersonic: Learning to Generate Source Code Optimizations in C/C++ (Zimin Chen, Sen Fang and Martin Monperrus), In IEEE Transactions on Software Engineering, 2024.
- CigaR: Cost-efficient Program Repair with LLMs (Dávid Hidvégi, Khashayar Etemadi, Sofia Bobadilla and Martin Monperrus), Technical report 2402.06598, arXiv, 2024.
- Mokav: Execution-driven Differential Testing with LLMs (Khashayar Etemadi, Bardia Mohammadi, Zhendong Su and Martin Monperrus), Technical report 2406.10375, arXiv, 2024.
- Galapagos: Automated N-Version Programming with LLMs (Javier Ron, Diogo Gaspar, Javier Cabrera-Arteaga, Benoit Baudry and Martin Monperrus), Technical report 2408.09536, arXiv, 2024.
- RepairBench: Leaderboard of Frontier Models for Program Repair (André Silva and Martin Monperrus), Technical report 2409.18952, arXiv, 2024.
2023
- Human, What Must I Tell You? (Markus Borg, Emil Aasa, Khashayar Etemadi and Martin Monperrus), In IEEE Software, Institute of Electrical and Electronics Engineers (IEEE), volume 40, 2023.
- Learning the Relation between Code Features and Code Transforms with Structured Prediction (Zhongxing Yu, Matias Martinez, Zimin Chen, Tegawendé F. Bissyandé and Martin Monperrus), In IEEE Transactions on Software Engineering, 2023.
- Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules (Steve Kommrusch, Martin Monperrus and Louis-Noël Pouchet), In IEEE Transactions on Software Engineering, 2023.
- MUFIN: Improving Neural Repair Models with Back-Translation (André Silva, João F. Ferreira, He Ye and Martin Monperrus), Technical report 2304.02301, arXiv, 2023.
- RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair (André Silva, Sen Fang and Martin Monperrus), Technical report 2312.15698, arXiv, 2023.
2022
- Neural Program Repair with Execution-based Backpropagation (He Ye, Matias Martinez and Martin Monperrus), In Proceedings of the International Conference on Software Engineering, 2022.
- Neural Transfer Learning for Repairing Security Vulnerabilities in C Code (Zimin Chen, Steve Kommrusch and Martin Monperrus), In IEEE Transactions on Software Engineering, 2022.
- SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics (He Ye, Matias Martinez, Xiapu Luo, Tao Zhang and Martin Monperrus), In Proceedings of ASE, 2022.
- Styler: learning formatting conventions to repair Checkstyle violations (Benjamin Loriot, Fernanda Madeiral and Martin Monperrus), In Empirical Software Engineering, 2022.
2021
2020
2019
- SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair (Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk and Martin Monperrus), In IEEE Transactions on Software Engineering, 2019.
- Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities (Martin White, Michele Tufano, Matias Martinez, Martin Monperrus and Denys Poshyvanyk), In Proceedings of the IEEE International Conference on Software Analysis, Evolution and Reengineering, 2019.
- A Literature Study of Embeddings on Source Code (Zimin Chen and Martin Monperrus), Technical report 1904.03061, arXiv, 2019.
2018
2016
- A Learning Algorithm for Change Impact Prediction (Vincenzo Musco, Antonin Carette, Martin Monperrus and Philippe Preux), In 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, 2016.
- BanditRepair: Speculative Exploration of Runtime Patches (Thomas Durieux, Youssef Hamadi and Martin Monperrus), Technical report 1603.07631, arXiv, 2016.
- Mutation-Based Graph Inference for Fault Localization (Vincenzo Musco, Martin Monperrus and Philippe Preux), In International Working Conference on Source Code Analysis and Manipulation, 2016.
2015
2014
2013
2012
2011
2010
2009