Overfitting in semantics-based automated program repair

The primary goal of Automated Program Repair (APR) is to automatically fix buggy software, to reduce the manual bug-fix burden that presently rests on human developers. Existing APR techniques can be generally divided into two families: semantics- vs. heuristics-based. Semantics-based APR uses symbo...

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Veröffentlicht in:Empirical software engineering : an international journal Jg. 23; H. 5; S. 3007 - 3033
Hauptverfasser: Le, Xuan Bach D., Thung, Ferdian, Lo, David, Goues, Claire Le
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2018
Springer Nature B.V
Schlagworte:
Automation
Compilers
Computer Science
Heuristic
Heuristic methods
Interpreters
Maintenance
Programming Languages
Quality assessment
Repair
Semantics
Software Engineering/Programming and Operating Systems
Automated program repair
Symbolic execution
Program synthesis
Patch overfitting
ISSN:1382-3256, 1573-7616
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Zusammenfassung:The primary goal of Automated Program Repair (APR) is to automatically fix buggy software, to reduce the manual bug-fix burden that presently rests on human developers. Existing APR techniques can be generally divided into two families: semantics- vs. heuristics-based. Semantics-based APR uses symbolic execution and test suites to extract semantic constraints, and uses program synthesis to synthesize repairs that satisfy the extracted constraints. Heuristic-based APR generates large populations of repair candidates via source manipulation, and searches for the best among them. Both families largely rely on a primary assumption that a program is correctly patched if the generated patch leads the program to pass all provided test cases. Patch correctness is thus an especially pressing concern. A repair technique may generate overfitting patches, which lead a program to pass all existing test cases, but fails to generalize beyond them. In this work, we revisit the overfitting problem with a focus on semantics-based APR techniques, complementing previous studies of the overfitting problem in heuristics-based APR. We perform our study using IntroClass and Codeflaws benchmarks, two datasets well-suited for assessing repair quality, to systematically characterize and understand the nature of overfitting in semantics-based APR. We find that similar to heuristics-based APR, overfitting also occurs in semantics-based APR in various different ways.
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ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-017-9577-2