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SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
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Prof. Martin Monperrus - COLLOQUIUM LECTURE -
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Monday, 6.1.2020, 14:30
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Room 337 Taub Bld.
Advisor:  KTH Royal Institute of Technology, Sweden
Host:  Eran Yahav
This talk presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, 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 it 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 950/4711 testing samples, and find correct patches for 14 bugs in Defects4J. It captures a wide range of repair operators without any domain-specific top-down design. See https://arxiv.org/pdf/1901.01808 Short Bio: ============= Martin Monperrus is Professor of Software Technology at KTH Royal Institute of Technology. He was previously associate professor at the University of Lille and affiliated researcher at Inria. He received a Ph.D. from the University of Rennes, and a Master's degree from the Compiegne University of Technology. His research lies in the field of software engineering with a current focus on automatic program repair, program hardening and chaos engineering. ========================================= Rereshments will be served from 14:15 Lecture starts at 14:30
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