The Strength of Random Search on Automated Program Repair

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Yuhua Qi and Xiaoguang Mao and Yan Lei and 
                 Ziying Dai and Chengsong Wang",
  title =        "The Strength of Random Search on Automated Program
  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, Automated
                 program repair, random search, search-based software
  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.",

Genetic Programming entries for Yuhua Qi Xiaoguang Mao Yan Lei Ziying Dai Chengsong Wang