Automatically finding patches using genetic programming

Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specif...

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Veröffentlicht in:2009 IEEE 31st International Conference on Software Engineering S. 364 - 374
Hauptverfasser: Weimer, Westley, Nguyen, ThanhVu, Le Goues, Claire, Forrest, Stephanie
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 16.05.2009
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Biological information theory
Biology computing
Computer bugs
Computing methodologies > Artificial intelligence > Search methodologies > Heuristic function construction
Costs
Debugging
Formal specifications
Genetic mutations
Genetic programming
Software and its engineering > Software creation and management > Software verification and validation > Software defect analysis > Software testing and debugging
Software and its engineering > Software notations and tools
Software maintenance
Testing
ISBN:9781424434534, 142443453X
ISSN:0270-5257
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Abstract Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report experimental results demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average.
AbstractList Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report experimental results demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average.
Author Weimer, Westley
Nguyen, ThanhVu
Forrest, Stephanie
Le Goues, Claire
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  surname: Forrest
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  organization: University of New Mexico, USA
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Snippet Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated...
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StartPage 364
SubjectTerms Biological information theory
Biology computing
Computer bugs
Computing methodologies -- Artificial intelligence -- Search methodologies -- Heuristic function construction
Costs
Debugging
Formal specifications
Genetic mutations
Genetic programming
Software and its engineering -- Software creation and management -- Software verification and validation -- Software defect analysis -- Software testing and debugging
Software and its engineering -- Software notations and tools
Software maintenance
Testing
Title Automatically finding patches using genetic programming
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