AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Liu:2019:SANER,

• author = "Kui Liu and Anil Koyuncu and Dongsun Kim and Tegawende F. Bissyande",
• title = "{AVATAR:} Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations",
• booktitle = "2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)",
• year = "2019",
• pages = "456--467",
• month = "24-27 " # feb,
• address = "Hangzhou, China",
• keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automated program repair, static analysis, fix pattern",
• isbn13 = "978-1-7281-0591-8",
• ISSN = "1534-5351",
• URL = "https://arxiv.org/abs/1812.07270",
• DOI = "doi:10.1109/SANER.2019.8667970",
• code_url = "https://github.com/SerVal-DTF/AVATAR",
• size = "12 pages",
• abstract = "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 paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the-art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.",
• notes = "Also known as \cite{8667970}",

}

Genetic Programming entries for Kui Liu Anil Koyuncu Dongsun Kim Tegawende F Bissyande

Citations