Title

Multi-Objective Coevolutionary Automated Software Correction

Presenter Information

James Bridges

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

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Apr 10th, 9:00 AM Apr 10th, 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.