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Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Proceedings of the 2012 International Symposium on Software Testing and Analysis
A systematic study of automated program repair: fixing 55 out of 105 bugs for $8 each
Proceedings of the 34th International Conference on Software Engineering
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
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Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Proceedings of the 2013 International Symposium on Software Testing and Analysis
Current challenges in automatic software repair
Software Quality Control
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Journal of Systems and Software
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ECOOP'13 Proceedings of the 27th European conference on Object-Oriented Programming
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HVC'12 Proceedings of the 8th international conference on Hardware and Software: verification and testing
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Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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This paper describes GenProg, an automated method for repairing defects in off-the-shelf, legacy programs without formal specifications, program annotations, or special coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given defect, using existing test suites to encode both the defect and required functionality. Structural differencing algorithms and delta debugging reduce the difference between this variant and the original program to a minimal repair. We describe the algorithm and report experimental results of its success on 16 programs totaling 1.25 M lines of C code and 120K lines of module code, spanning eight classes of defects, in 357 seconds, on average. We analyze the generated repairs qualitatively and quantitatively to demonstrate that the process efficiently produces evolved programs that repair the defect, are not fragile input memorizations, and do not lead to serious degradation in functionality.