Applying Multi-Objective Genetic Algorithm for Efficient Selection on Program Generation

Automated program generation (APG) is a concept of automatically making a computer program. Toward this goal, transferring automated program repair (APR) to APG can be considered. APR modifies the buggy input source code to pass all test cases. APG regards empty source code as initially failing all test cases, i.e., containing multiple bugs. Search-based APR repeatedly generates program variants and evaluates them. Many traditional APR systems evaluate the fitness of variants based on the number of passing test cases. However, when source code contains multiple bugs, this fitness function lacks the expressive power of variants. In this paper, we propose the application of a multi-objective genetic algorithm to APR in order to improve efficiency. We also propose a new crossover method that combines two variants with complementary test results, taking advantage of the high expressive power of multi-objective genetic algorithms for evaluation. We tested the effectiveness of the proposed method on competitive programming tasks. The obtained results showed significant differences in the number of successful trials and the required generation time.