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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Vydáno v:	Empirical software engineering : an international journal Ročník 23; číslo 5; s. 3007 - 3033
Hlavní autoři:	Le, Xuan Bach D., Thung, Ferdian, Lo, David, Goues, Claire Le
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.10.2018 Springer Nature B.V
Témata:	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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Abstract	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.
AbstractList	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. 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.
Author	Goues, Claire Le Lo, David Thung, Ferdian Le, Xuan Bach D.
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Cites_doi	10.1109/ICSE.2013.6606626 10.1109/ASE.2015.83 10.1145/2837614.2837617 10.1145/2568225.2568254 10.1109/ICSME.2016.68 10.1145/2786805.2786811 10.1145/3106237.3106309 10.1109/TSE.2016.2560811 10.1109/TSE.2011.104 10.1145/2884781.2884872 10.1109/ICPC.2015.15 10.1145/1735223.1735249 10.1145/2970276.2975934 10.1109/TSE.2015.2454513 10.1145/2884781.2884807 10.1109/SANER.2016.76 10.1109/TAIC.PART.2007.13 10.1007/s10664-013-9282-8 10.1145/2398857.2384626 10.1145/2593735.2593740 10.1109/ISSRE.2015.7381836 10.1007/978-3-319-21668-3_12 10.1145/2771783.2771791 10.1109/ASE.2013.6693094 10.1109/ASE.2015.60 10.1109/ICSE.2015.63 10.1145/1806799.1806833 10.1145/2610384.2628055 10.1145/2786805.2786825 10.1109/ICSE.2013.6606623 10.1109/ICSE.2012.6227211 10.1109/ICSME.2016.66 10.1145/2338965.2336775 10.1145/3092703.3098225
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References_xml	– reference: Könighofer R, Bloem R (2011) Automated error localization and correction for imperative programs. In: Formal methods in computer-aided design, IEEE, FMCAD, pp 91–100 – reference: Nguyen HD T, Qi D, Roychoudhury A, Chandra S (2013) Semfix: Program repair via semantic analysis. In: Proceedings of the 2013 international conference on software engineering, ICSE, pp 772–781 – reference: Le Goues C, Dewey-Vogt M, Forrest S, Weimer W (2012) A systematic study of automated program repair: fixing 55 out of 105 bugs for $8 each. In: 2012 34th international conference on software engineering (ICSE). IEEE, pp 3–13 – reference: Tan SH, Yi J, Yulis, Mechtaev S, Roychoudhury A (2017) Codeflaws: a programming competition benchmark for evaluating automated program repair tools. In: ICSE Poster, to appear – reference: Le GouesCHoltschulteNSmithEKBrunYDevanbuPForrestSWeimerWThe ManyBugs and IntroClass benchmarks for automated repair of C programsIEEE Trans Softw Eng201541121236125610.1109/TSE.2015.2454513 – reference: Kim D, Nam J, Song J, Kim S (2013) Automatic patch generation learned from human-written patches. In: ACM/IEEE international conference on software engineering, ICSE, pp 802–811 – reference: Fry ZP, Landau B, Weimer W (2012) A human study of patch maintainability. In: International symposium on software testing and analysis (ISSTA), pp 177–187 – reference: Le X BD, Chu DH, Lo D, Le Goues C, Visser W (2017b) S3: syntax-and semantic-guided repair synthesis via programming by examples. In: Joint meeting of the european software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering. ACM, pp 593–604 – reference: Reynolds A, Deters M, Kuncak V, Tinelli C, Barrett C (2015) Counterexample-guided quantifier instantiation for synthesis in SMT. In: International conference on computer aided verification. Springer, pp 198–216 – reference: Le X BD, Lo D, Le Goues C (2016b) Empirical study on synthesis engines for semantics-based program repair. In: International conference on software maintenance and evolution (ICSME). IEEE, pp 423–427 – reference: Jha S, Gulwani S, Seshia SA, Tiwari A (2010) Oracle-guided component-based program synthesis. In: Proceedings of the 32nd ACM/IEEE international conference on software engineering, ICSE, pp 215–224 – reference: Just R, Jalali D, Ernst MD (2014) Defects4j: a database of existing faults to enable controlled testing studies for java programs. 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Snippet	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...
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SubjectTerms	Automation Compilers Computer Science Heuristic Heuristic methods Interpreters Maintenance Programming Languages Quality assessment Repair Semantics Software Engineering/Programming and Operating Systems
Title	Overfitting in semantics-based automated program repair
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