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Multi-objective Coevolutionary Automated Software Correction
Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (GECCO 2012)
  • Josh L. Wilkerson
  • Daniel R. Tauritz, Missouri University of Science and Technology
  • James M. Bridges

For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. The Coevolutionary Automated Software Correction (CASC) system targets the correction phase by coevolving test cases and programs at the source code level. This paper presents the latest version of the CASC system featuring multi-objective optimization and an enhanced representation language. Results are presented demonstrating CASC's ability to successfully correct five seeded bugs in two non-trivial programs from the Siemens test suite. Additionally, evidence is provided substantiating the hypothesis that multi-objective optimization is beneficial to SBSE.

Meeting Name
14th International Conference on Genetic and Evolutionary Computation, GECCO'12 (2012: Jul. 7-11, Philadelphia, PA)
Computer Science
Keywords and Phrases
  • Automated Program Correction,
  • Coevolution,
  • Fitness Sharing,
  • Genetic Programming,
  • Multi-Objective Optimization,
  • NSGA-II,
  • SBSE
International Standard Book Number (ISBN)
Document Type
Article - Conference proceedings
Document Version
File Type
© 2012 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
Citation Information
Josh L. Wilkerson, Daniel R. Tauritz and James M. Bridges. "Multi-objective Coevolutionary Automated Software Correction" Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (GECCO 2012) (2012) p. 1229 - 1236
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