A genetic programming approach to automated software repair

  • Authors:

  • Stephanie Forrest;ThanhVu Nguyen;Westley Weimer;Claire Le Goues

  • Affiliations:

  • University of New Mexico, Albuquerque, NM, USA;University of New Mexico, Albuquerque, NM, USA;University of Virginia, Charlottesville, VA, USA;University of Virginia, Charlottesville, VA, USA

  • Venue:
  • Proceedings of the 11th Annual conference on Genetic and evolutionary computation
  • Year:
  • 2009

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Abstract

Genetic programming is combined with program analysis methods to repair bugs in off-the-shelf legacy C programs. Fitness is defined using negative test cases that exercise the bug to be repaired and positive test cases that encode program requirements. Once a successful repair is discovered, structural differencing algorithms and delta debugging methods are used to minimize its size. Several modifications to the GP technique contribute to its success: (1) genetic operations are localized to the nodes along the execution path of the negative test case; (2) high-level statements are represented as single nodes in the program tree; (3) genetic operators use existing code in other parts of the program, so new code does not need to be invented. The paper describes the method, reviews earlier experiments that repaired 11 bugs in over 60,000 lines of code, reports results on new bug repairs, and describes experiments that analyze the performance and efficacy of the evolutionary components of the algorithm.