AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations
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gp-bibliography.bib Revision:1.7954
- @InProceedings{Liu:2019:SANER,
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author = "Kui Liu and Anil Koyuncu and Dongsun Kim and
Tegawende F. Bissyande",
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title = "{AVATAR:} Fixing Semantic Bugs with Fix Patterns of
Static Analysis Violations",
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booktitle = "2019 IEEE 26th International Conference on Software
Analysis, Evolution and Reengineering (SANER)",
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year = "2019",
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pages = "456--467",
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month = "24-27 " # feb,
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address = "Hangzhou, China",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, APR, Automated program repair, static
analysis, fix pattern",
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isbn13 = "978-1-7281-0591-8",
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ISSN = "1534-5351",
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URL = "https://arxiv.org/abs/1812.07270",
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DOI = "doi:10.1109/SANER.2019.8667970",
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code_url = "https://github.com/SerVal-DTF/AVATAR",
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size = "12 pages",
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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.",
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notes = "Also known as \cite{8667970}",
- }
Genetic Programming entries for
Kui Liu
Anil Koyuncu
Dongsun Kim
Tegawende F Bissyande
Citations