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Reliable Fix Patterns Inferred from Static Checkers for Automated Program Repair,
Discipline: Informatique et sciences de l'information
Auteur(s): Kui Liu, Jingtang Zhang, Li Li, Anil Koyuncu, Dongsun Kim, Chunpeng Ge, Zhe Liu, Jacques Klein, Tegawendé F Bissyandé
Renseignée par : BISSYANDE T. François D'Assise
Résumé

Fix pattern-based patch generation is a promising direction in automated program repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this article, we propose investigating the possibility in an APR scenario of leveraging fix patterns inferred from code changes that address violations detected by static analysis tools. To that end, we build a fix pattern-based APR tool, Avatar, which exploits fix patterns of static analysis violations as ingredients for the patch generation of repairing semantic bugs. Evaluated on four benchmarks (i.e., Defects4J, Bugs.jar, BEARS)

Mots-clés

program repair, fix patterns

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