Towards Efficient Program Repair with APR Tools Based on Genetic Algorithms

Automated program repair (APR) is expected to reduce manual debugging effort in software development. However, its tools have seldom been adopted in real-world software development. One of the reasons for this is likely that the tools frequently output repaired programs with low readability, which many developers are unwilling to accept. To reveal a situation where such programs are output during repairing, we experimented with an APR tool based on a genetic algorithm to fix bugs in an open-source Java project. Experimental results show that many acceptable programs are output in a relatively early stage of the repair process. Additionally, increasing the number of variants generated per generation is beneficial for obtaining a greater number of acceptable programs with the same repair time. These findings help developers use the APR tool efficiently, avoiding needlessly lengthening the repair time.