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An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation

https://doi.org/10.1109/SANER53432.2022.00069

Moran, Kevin; Yachnes, Ali; Purnell, George; Mahmud, Junayed; Tufano, Michele; Cardenas, Carlos Bernal; Poshyvanyk, Denys; H'Doubler, Zach (March 2022, SANER'22)

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DeepMutation: A Neural Mutation Tool

Tufano, Michele and (October 2020, 2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion))

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Towards Automating Code Review Activities

https://doi.org/10.1109/ICSE43902.2021.00027

Tufano, Rosalia; Pascarella, Luca; Tufano, Michele; Poshyvanyk, Denys; Bavota, Gabriele (May 2021, ICSE'21)

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Enabling Mutant Generation for Open- and Closed-Source Android Apps

https://doi.org/10.1109/TSE.2020.2982638

Escobar-Velasquez, Camilo; Linares-Vasquez, Mario; Bavota, Gabriele; Tufano, Michele; Moran, Kevin; Di Penta, Massimiliano; Vendome, Christopher; Bernal-Cardenas, Carlos; Poshyvanyk, Denys (January 2022, IEEE Transactions on Software Engineering)

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An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

https://doi.org/10.1145/3340544

Tufano, Michele; Watson, Cody; Bavota, Gabriele; Penta, Massimiliano Di; White, Martin; Poshyvanyk, Denys (October 2019, ACM Transactions on Software Engineering and Methodology)

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SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

https://doi.org/10.1109/TSE.2019.2940179

Chen, Zimin; Kommrusch, Steve James; Tufano, Michele; Pouchet, Louis-Noel; Poshyvanyk, Denys; Monperrus, Martin (September 2019, IEEE Transactions on Software Engineering)

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design.
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