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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Published in:	Empirical software engineering : an international journal Vol. 23; no. 5; pp. 3007 - 3033
Main Authors:	Le, Xuan Bach D., Thung, Ferdian, Lo, David, Goues, Claire Le
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2018 Springer Nature B.V
Subjects:	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.
Author_xml	– sequence: 1 givenname: Xuan Bach D. orcidid: 0000-0001-5044-1582 surname: Le fullname: Le, Xuan Bach D. email: dxb.le.2013@phdis.smu.edu.sg organization: School of Information Systems, Singapore Management University – sequence: 2 givenname: Ferdian surname: Thung fullname: Thung, Ferdian organization: School of Information Systems, Singapore Management University – sequence: 3 givenname: David surname: Lo fullname: Lo, David organization: School of Information Systems, Singapore Management University – sequence: 4 givenname: Claire Le surname: Goues fullname: Goues, Claire Le organization: School of Computer Science, Carnegie Mellon University
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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	Just R, Jalali D, Ernst MD (2014) Defects4j: a database of existing faults to enable controlled testing studies for java programs. In: Proceedings of the 2014 international symposium on software testing and analysis, ISSTA, pp 437–440 LogozzoFBallTModular and verified automatic program repairSIGPLAN Not2012471013314610.1145/2398857.2384626 Ke Y, Stolee KT, Le Goues C, Brun Y (2015) Repairing programs with semantic code search. In: Proceedings of the 30th IEEE/ACM international conference on automated software engineering, pp 295–306 AlurRBodikRJuniwalGMartinMMRaghothamanMSeshiaSASinghRSolar-LezamaATorlakEUdupaASyntax-guided synthesisDependable Software Systems Engineering201540125 Le X BD, Le TDB, Lo D (2015b) Should fixing these failures be delegated to automated program repair?. In: International symposium on software reliability engineering (ISSRE). IEEE, pp 427–437 Le X BD, Lo D, Le Goues C (2016c) History driven program repair. In: Proceedings of the 23rd IEEE international conference on software analysis, evolution, and reengineering, SANER, pp 213–224 Le X BD, Chu D H, David L, Le Goues C (2017a) Jfix: sematics-based repair of java programs via symbolic pathfinder. In: Proceedings of the 2017 ACM international symposium on software testing and analysis, ISSTA WeimerWForrestSLe GouesCNguyenTAutomatic program repair with evolutionary computationCommun ACM201053510911610.1145/1735223.1735249 Long F, Rinard M (2016b) Automatic patch generation by learning correct code. In: ACM SIGPLAN notices, vol 51. ACM, pp 298–312 Le X BD, Le Q L, Lo D, Le Goues C (2016a) Enhancing automated program repair with deductive verification. In: International conference on software maintenance and evolution (ICSME). IEEE, pp 428–432 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 DeMarco F, Xuan J, Le Berre D, Monperrus M (2014) Automatic repair of buggy if conditions and missing preconditions with SMT. In: Proceedings of the 6th international workshop on constraints in software testing, verification, and analysis, pp 30–39 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 Cadar C, Dunbar D, Engler DR et al. (2008) Klee: unassisted and automatic generation of high-coverage tests for complex systems programs. In: Operating systems design and implementation, OSDI, pp 209–224 Smith EK, Barr ET, Le Goues C, Brun Y (2015) Is the cure worse than the disease? Overfitting in automated program repair. In: Joint meeting of the european software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering, ACM, ESEC/FSE, pp 532–543 Thung F, Le X BD, Lo D (2015) Active semi-supervised defect categorization. In: International conference on program comprehension (ICPC). IEEE Press, pp 60–70 Le XBD (2016) Towards efficient and effective automatic program repair. In: International conference on automated software engineering (ASE). IEEE, pp 876–879 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 Mechtaev S, Yi J, Roychoudhury A (2015) Directfix: Looking for simple program repairs. In: Proceedings of the 37th ACM/IEEE international conference on software engineering, ICSE, pp 448–458 Le GouesCHoltschulteNSmithEKBrunYDevanbuPForrestSWeimerWThe ManyBugs and IntroClass benchmarks for automated repair of C programsIEEE Trans Softw Eng201541121236125610.1109/TSE.2015.2454513 Könighofer R, Bloem R (2012) Repair with on-the-fly program analysis. In: Haifa verification conference. Springer, pp 56–71 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 Long F, Rinard M (2016a) An analysis of the search spaces for generate and validate patch generation systems. In: Proceedings of the 38th international conference on software engineering. ACM, pp 702–713 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 Xuan J, Martinez M, DeMarco F, Clément M, Lamelas S, Durieux T, Le Berre D, Monperrus M (2016) Nopol: Automatic repair of conditional statement bugs in java programs. IEEE Trans Softw Eng https://doi.org/10.1109/TSE.2016.2560811. https://hal.archives-ouvertes.fr/hal-01285008/document Long F, Rinard M (2015) Staged program repair with condition synthesis. In: Joint meeting of the european software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering. ACM, pp 166–178 Qi Z, Long F, Achour S, Rinard M (2015) An analysis of patch plausibility and correctness for generate-and-validate patch generation systems. In: Proceedings of the 2015 international symposium on software testing and analysis, ISSTA, pp 24–36 Le TDB, Le XBD, Lo D, Beschastnikh I (2015a) Synergizing specification miners through model fissions and fusions. In: International conference on automated software engineering (ASE). IEEE, pp 115–125 Tassey G (2002) The economic impacts of inadequate infrastructure for software testing. National Institute of Standards and Technology, RTI project 7007(011) 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 Qi Y, Mao X, Lei Y, Dai Z, Wang C (2014) The strength of random search on automated program repair. In: Proceedings of the 36th international conference on software engineering, ACM, ICSE, pp 254–265 Weimer W, Fry ZP, Forrest S (2013) Leveraging program equivalence for adaptive program repair: models and first results. In: Proceedings of the 28th international conference on automated software engineering, IEEE, ASE, pp 356–366 Gulwani S, Esparza J, Grumberg O, Sickert S (2016) Programming by examples (and its applications in data wrangling). Verification and synthesis of correct and secure systems MartinezMMonperrusMMining software repair models for reasoning on the search space of automated program fixingEmpir Softw Eng201520117620510.1007/s10664-013-9282-8 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 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 Mechtaev S, Yi J, Roychoudhury A (2016) Angelix: scalable multiline program patch synthesis via symbolic analysis. In: Proceedings of the 38th ACM/IEEE international conference on software engineering, ICSE, pp 691–701 Abreu R, Zoeteweij P, Van Gemund AJ (2007) On the accuracy of spectrum-based fault localization. In: Testing: academic and industrial conference practice and research techniques-MUTATION, 2007. TAICPART-MUTATION 2007. IEEE, pp 89–98 Le GouesCNguyenTForrestSWeimerWGenprog: a generic method for automatic software repairIEEE Trans Softw Eng2012381547210.1109/TSE.2011.104 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 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 W Weimer (9577_CR39) 2010; 53 9577_CR1 C Le Goues (9577_CR23) 2015; 41 9577_CR40 9577_CR41 9577_CR26 9577_CR27 9577_CR25 9577_CR20 9577_CR21 9577_CR29 9577_CR30 R Alur (9577_CR2) 2015; 40 F Logozzo (9577_CR24) 2012; 47 9577_CR15 9577_CR37 9577_CR16 9577_CR38 9577_CR13 9577_CR35 9577_CR14 9577_CR36 9577_CR11 9577_CR33 9577_CR12 9577_CR34 9577_CR31 9577_CR10 9577_CR32 9577_CR5 9577_CR4 9577_CR3 9577_CR9 9577_CR19 M Martinez (9577_CR28) 2015; 20 9577_CR8 C Le Goues (9577_CR22) 2012; 38 9577_CR7 9577_CR17 9577_CR6 9577_CR18
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. In: Proceedings of the 2014 international symposium on software testing and analysis, ISSTA, pp 437–440 – reference: Qi Y, Mao X, Lei Y, Dai Z, Wang C (2014) The strength of random search on automated program repair. In: Proceedings of the 36th international conference on software engineering, ACM, ICSE, pp 254–265 – reference: DeMarco F, Xuan J, Le Berre D, Monperrus M (2014) Automatic repair of buggy if conditions and missing preconditions with SMT. In: Proceedings of the 6th international workshop on constraints in software testing, verification, and analysis, pp 30–39 – reference: Le X BD, Le Q L, Lo D, Le Goues C (2016a) Enhancing automated program repair with deductive verification. In: International conference on software maintenance and evolution (ICSME). IEEE, pp 428–432 – reference: Qi Z, Long F, Achour S, Rinard M (2015) An analysis of patch plausibility and correctness for generate-and-validate patch generation systems. 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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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