- author = "Yuhua Qi and Xiaoguang Mao and Yan Lei and Ziying Dai and Chengsong Wang",
- title = "The Strength of Random Search on Automated Program Repair",
- booktitle = "Proceedings of the 36th International Conference on Software Engineering, ICSE 2014",
- year = "2014",
- pages = "254--265",
- address = "Hyderabad, India",
- keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Automated program repair, random search, search-based software engineering",
- isbn13 = "978-1-4503-2756-5",
- acmid = "2568254",
-
DOI = "
doi:10.1145/2568225.2568254",
- size = "12",
-
abstract = "Automated program repair recently received
considerable attentions, and many techniques on this
research area have been proposed. Among them, two
genetic-programming-based techniques, GenProg and Par,
have shown the promising results. In particular,
GenProg has been used as the baseline technique to
check the repair effectiveness of new techniques in
much literature. Although GenProg and Par have shown
their strong ability of fixing real-life bugs in
nontrivial programs, to what extent GenProg and Par can
benefit from genetic programming, used by them to guide
the patch search process, is still unknown.
To address the question, we present a new automated repair technique using random search, which is commonly considered much simpler than genetic programming, and implement a prototype tool called RSRepair. Experiment on 7 programs with 24 versions shipping with real-life bugs suggests that RSRepair, in most cases (23/24), outperforms GenProg in terms of both repair effectiveness (requiring fewer patch trials) and efficiency (requiring fewer test case executions), justifying the stronger strength of random search over genetic programming. According to experimental results, we suggest that every proposed technique using optimization algorithm should check its effectiveness by comparing it with random search.",
