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. The latest version of the CASC system is presented, featuring multiobjective optimization and an enhanced representation language. Results are presented demonstrating CASC’s ability to successfully correct five seeded bugs in two non-trivial programs the Siemens test suite. Additionally, evidence is provided substantiating the hypothesis that multiobjective optimization is beneficial to SBSE

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