Multi-Objective Coevolutionary Automated Software Correction
Department
Computer Science
Major
Computer Science
Research Advisor
Tauritz, Daniel R.
Advisor's Department
Computer Science
Funding Source
Missouri S&T Opportunities for Undergraduate Research Experiences (OURE) Program
Abstract
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 and testing phases by coevolving test cases and programs at the source code level. Programs and tests are evaluated by objectives defined by the user, and each population is optimized for these objectives. Results presented demonstrate CASC's ability to successfully correct five seeded bugs in two non-trivial programs from the Siemens test suite. Additionally, empirical evidence is provided substantiating the hypothesis that multi-objective optimization is beneficial to SBSE as compared to traditional single-objective approaches.
Biography
James is a Senior at Missouri University of Science and Technology majoring in both Computer Science and Applied Mathematics with an emphasis in Computational Mathematics; graduating in the spring of 2012. His research interests include evolutionary algorithms, multi-objective optimization, artificial intelligence, and search based software engineering.
Research Category
Sciences
Presentation Type
Poster Presentation
Document Type
Poster
Location
Upper Atrium/Hallway
Presentation Date
10 Apr 2012, 9:00 am - 11:45 am
Multi-Objective Coevolutionary Automated Software Correction
Upper Atrium/Hallway
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 and testing phases by coevolving test cases and programs at the source code level. Programs and tests are evaluated by objectives defined by the user, and each population is optimized for these objectives. Results presented demonstrate CASC's ability to successfully correct five seeded bugs in two non-trivial programs from the Siemens test suite. Additionally, empirical evidence is provided substantiating the hypothesis that multi-objective optimization is beneficial to SBSE as compared to traditional single-objective approaches.
