Experience report: How do techniques, programs, and tests impact automated program repair?

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Kong:2015:ieeeISSRE,
  author =       "Xianglong Kong and Lingming Zhang and W. Eric Wong and 
                 Bixin Li",
  booktitle =    "26th IEEE International Symposium on Software
                 Reliability Engineering (ISSRE)",
  title =        "Experience report: How do techniques, programs, and
                 tests impact automated program repair?",
  year =         "2015",
  pages =        "194--204",
  address =      "Gaithersbury, MD, USA",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  DOI =          "doi:10.1109/ISSRE.2015.7381813",
  month =        "2-5 " # nov,
  abstract =     "Automated program repair can save tremendous manual
                 efforts in software debugging. Therefore, a huge body
                 of research efforts have been dedicated to design and
                 implement automated program repair techniques. Among
                 the existing program repair techniques,
                 genetic-programming-based techniques have shown
                 promising results. Recently, researchers found that
                 random-search-based and adaptive program repair
                 techniques can also produce effective results. In this
                 work, we performed an extensive study for four program
                 repair techniques, including genetic-programming-based,
                 random-search-based, brute-force-based and adaptive
                 program repair techniques. Due to the extremely large
                 time cost of the studied techniques, the study was
                 performed on 153 bugs from 9 small to medium sized
                 programs. In the study, we further investigated the
                 impacts of different programs and test suites on
                 effectiveness and efficiency of program repair
                 techniques. We found that techniques that work well
                 with small programs become too costly or ineffective
                 when applied to medium sized programs. We also computed
                 the false positive rates and discussed the ratio of the
                 explored search space to the whole search space for
                 each studied technique. Surprisingly, all the studied
                 techniques except the random-search-based technique are
                 consistent with the 80/20 rule, i.e., about 80percent
                 of successful patches are found within the first
                 20percent of search space.",
  notes =        "Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing,
                 China

                 Also known as \cite{7381813}",
}

Genetic Programming entries for Xianglong Kong Lingming Zhang W Eric Wong Bixin Li

Citations