research-article

Representations and operators for improving evolutionary software repair

Authors:

Claire Le Goues,

Westley Weimer,

Stephanie ForrestAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 959 - 966

https://doi.org/10.1145/2330163.2330296

Published: 07 July 2012 Publication History

Abstract

Evolutionary computation is a promising technique for automating time-consuming and expensive software maintenance tasks, including bug repair. The success of this approach, however, depends at least partially on the choice of representation, fitness function, and operators. Previous work on evolutionary software repair has employed different approaches, but they have not yet been evaluated in depth. This paper investigates representation and operator choices for source-level evolutionary program repair in the GenProg framework [17], focusing on: (1) representation of individual variants, (2) crossover design, (3) mutation operators, and (4) search space definition. We evaluate empirically on a dataset comprising 8 C programs totaling over 5.1 million lines of code and containing 105 reproducible, human-confirmed defects. Our results provide concrete suggestions for operator and representation design choices for evolutionary program repair. When augmented to incorporate these suggestions, GenProg repairs 5 additional bugs (60 vs. 55 out of 105), with a decrease in repair time of 17-43% for the more difficult repair searches.

References

[1]

T. Ackling, B. Alexander, and I. Grunert. Evolving patches for software repair. In Genetic and Evolutionary Computation, pages 1427--1434, 2011.

Digital Library

[2]

R. Al-Ekram, A. Adma, and O. Baysal. diffX: an algorithm to detect changes in multi-version XML documents. In Conference of the Centre for Advanced Studies on Collaborative research, pages 1--11. IBM Press, 2005.

Digital Library

[3]

E. Alba and F. Chicano. Finding safety errors with ACO. In Genetic and Evolutionary Computation Conference, pages 1066--1073, 2007.

Digital Library

[4]

A. Arcuri. Evolutionary repair of faulty software. Applied Soft Computing, 11(4):3494--3514, June 2011.

Digital Library

[5]

A. Arcuri and X. Yao. A novel co-evolutionary approach to automatic software bug fixing. In IEEE Congress on Evolutionary Computation, pages 162--168, 2008.

[6]

A. Barreto, M. de O. Barros, and C. M. Werner. Staffing a software project: a constraint satisfaction and optimization-based approach. Computers and Operations Research, 35(10):3073--3089, 2008.

Digital Library

[7]

V. Debroy and W. E. Wong. Using mutation to automatically suggest fixes for faulty programs. In International Conference on Software Testing, Verification, and Validation, pages 65--74, 2010.

Digital Library

[8]

L. Erlikh. Leveraging legacy system dollars for e-business. IT Professional, 2(3):17--23, 2000.

Digital Library

[9]

E. Fast, C. Le Goues, S. Forrest, and W. Weimer. Designing better fitness functions for automated program repair. In Genetic and Evolutionary Computation Conference, 2010.

Digital Library

[10]

S. Forrest, W. Weimer, T. Nguyen, and C. Le Goues. A genetic programming approach to automated software repair. In Genetic and Evolutionary Computation Conference, pages 947--954, 2009.

Digital Library

[11]

M. Harman. The current state and future of search based software engineering. In International Conference on Software Engineering, pages 342--357, 2007.

Digital Library

[12]

J. A. Jones and M. J. Harrold. Empirical evaluation of the Tarantula automatic fault-localization technique. In Automated Software Engineering, pages 273--282, 2005.

Digital Library

[13]

R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence, 14(2):1137--1145, 1995.

Digital Library

[14]

W. B. Langdon and M. Harman. Evolving a CUDA kernel from an nVidia template. In Congress on Evolutionary Computation, pages 1--8, 2010.

[15]

W. B. Langdon, M. Harman, and Y. Jia. Efficient multi-objective higher order mutation testing with genetic programming. Journal of Systems and Software, 83(12):2416--2430, 2010.

Digital Library

[16]

C. Le Goues, M. Dewey-Vogt, S. Forrest, and W. Weimer. A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each. In International Conference on Software Engineering (to appear), 2012.

Digital Library

[17]

C. Le Goues, T. Nguyen, S. Forrest, and W. Weimer. GenProg: A generic method for automated software repair. Transactions on Software Engineering, 38(1):54--72, 2012.

Digital Library

[18]

C. C. Michael, G. McGraw, and M. A. Schatz. Generating software test data by evolution. Transactions on Software Engineering, 27(12):1085--1110, 2001.

Digital Library

[19]

M. Orlov and M. Sipper. Flight of the finch through the java wilderness. IEEE Transactions on Evolutionary Computation, 15(2):166--192.

Digital Library

[20]

M. Orlov and M. Sipper. Genetic programming in the wild: Evolving unrestricted bytecode. In Genetic and Evolutionary Computation Conference, pages 1043--1050, 2009.

Digital Library

[21]

J. E. Rowe and N. F. McPhee. The effects of crossover and mutation operators on variable length linear structures. In Genetic and Evolutionary Computation Conference, pages 535--542, 2001.

[22]

E. Schulte, S. Forrest, and W. Weimer. Automatic program repair through the evolution of assembly code. In Automated Software Engineering, pages 33--36, 2010.

Digital Library

[23]

R. C. Seacord, D. Plakosh, and G. A. Lewis. Modernizing Legacy Systems: Software Technologies, Engineering Process and Business Practices. Addison-Wesley Longman Publishing Co., Inc., 2003.

Digital Library

[24]

O. Seng, J. Stammel, and D. Burkhart. Search-based determination of refactorings for improving the class structure of object-oriented systems. In Genetic and Evolutionary Computation Conference, pages 1909--1916, 2006.

Digital Library

[25]

P. Sitthi-Amorn, N. Modly, W. Weimer, and J. Lawrence. Genetic programming for shader simplification. ACM Transactions on Graphics, 30(5), 2011.

Digital Library

[26]

S. Wappler and J. Wegener. Evolutionary unit testing of object-oriented software using strongly-typed genetic programming. In Genetic and Evolutionary Computation Conference, pages 1925--1932, 2006.

Digital Library

[27]

W. Weimer. Patches as better bug reports. In Generative Programming and Component Engineering, pages 181--190, 2006.

Digital Library

[28]

W. Weimer, T. Nguyen, C. Le Goues, and S. Forrest. Automatically finding patches using genetic programming. In International Conference on Software Engineering, pages 364--367, 2009.

Digital Library

[29]

D. R. White, A. Arcuri, and J. A. Clark. Evolutionary improvement of programs. IEEE Trans. on Evolutionary Computation, 15(4):515--538, aug. 2011.

Digital Library

[30]

A. Zeller. Yesterday, my program worked. Today, it does not. Why? In Foundations of Software Engineering, 1999.

Digital Library

Cited By

Santiesteban PHuang YWeimer WAhmad H(2023)CirFix: Automated Hardware Repair and its Real-World ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2023.326989949:7(3736-3752)Online publication date: Jul-2023
https://doi.org/10.1109/TSE.2023.3269899
Reid BTreude CWagner M(2023)Using the TypeScript compiler to fix erroneous Node.js snippets2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM59687.2023.00031(220-230)Online publication date: 2-Oct-2023
https://doi.org/10.1109/SCAM59687.2023.00031
Zhang YLeach KHuang Y(2023)Leveraging Evidence Theory to Improve Fault Localization: An Exploratory Study2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1109/ESEM56168.2023.10304791(1-12)Online publication date: 26-Oct-2023
https://doi.org/10.1109/ESEM56168.2023.10304791
Show More Cited By

Index Terms

Representations and operators for improving evolutionary software repair
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 07 July 2012

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GECCO '12

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Cited By

Santiesteban PHuang YWeimer WAhmad H(2023)CirFix: Automated Hardware Repair and its Real-World ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2023.326989949:7(3736-3752)Online publication date: Jul-2023
https://doi.org/10.1109/TSE.2023.3269899
Reid BTreude CWagner M(2023)Using the TypeScript compiler to fix erroneous Node.js snippets2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM59687.2023.00031(220-230)Online publication date: 2-Oct-2023
https://doi.org/10.1109/SCAM59687.2023.00031
Zhang YLeach KHuang Y(2023)Leveraging Evidence Theory to Improve Fault Localization: An Exploratory Study2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1109/ESEM56168.2023.10304791(1-12)Online publication date: 26-Oct-2023
https://doi.org/10.1109/ESEM56168.2023.10304791
Petke JAlexander BBarr EBrownlee AWagner MWhite D(2023)Program transformation landscapes for automated program modification using GinEmpirical Software Engineering10.1007/s10664-023-10344-528:4Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1007/s10664-023-10344-5
Licorish SWagner MWagner M(2022)Dissecting copy/delete/replace/swap mutationsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533970(1940-1945)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533970
Ahmad HHuang YWeimer WFalsafi BFerdman MLu SWenisch T(2022)CirFix: automatically repairing defects in hardware design codeProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507763(990-1003)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507763
Motwani MSoto MBrun YJust RLe Goues C(2022)Quality of Automated Program Repair on Real-World DefectsIEEE Transactions on Software Engineering10.1109/TSE.2020.299878548:2(637-661)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TSE.2020.2998785
Callan JKrauss OPetke JSarro F(2022)How do Android developers improve non-functional properties of software?Empirical Software Engineering10.1007/s10664-022-10137-227:5Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10664-022-10137-2
Ahmad HCashin PForrest SWeimer W(2022)Digging into Semantics: Where Do Search-Based Software Repair Methods Search?Parallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_1(3-18)Online publication date: 15-Aug-2022
https://doi.org/10.1007/978-3-031-14721-0_1
Blot APetke J(2021)Empirical Comparison of Search Heuristics for Genetic Improvement of SoftwareIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.307027125:5(1001-1011)Online publication date: Oct-2021
https://doi.org/10.1109/TEVC.2021.3070271
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