Abstract
Abstract Automated program repair (APR) techniques locate and fix faults automatically. In order to fix faults, APR applies a set of program modification operators (PMOs) to modify faulty programs. A potential repair is found when APR applies a PMO that fixes a fault. A brute-force search algorithm applies all PMOs in a predefined order until a potential repair is found. Brute-force can guarantee a fix but lowers APR performance, especially when it uses many PMOs. Stochastic search algorithms, such as a genetic algorithm, efficiently search the modifications space for a PMO that fixes a fault. In this paper, we conduct a comprehensive evaluation of the impact on APR effectiveness, APR performance, and the quality of potential repairs of three stochastic search algorithms:(1) a genetic algorithm (GA), (2) a genetic algorithm without a crossover operator (GAWoCross), and (3) a random search (RS). Our evaluation using 41 faulty versions of six different C programs shows that RS improves APR effectiveness and performance, but GA and GAWoCross improve the quality of potential repairs by generating more validated repairs, and potential repairs that failed fewer regression tests compared to RS.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
