Should fixing these failures be delegated to automated program repair?

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Xuan-Bach D. Le and Tien-Duy B. Le and David Lo",
  booktitle =    "26th IEEE International Symposium on Software
                 Reliability Engineering (ISSRE)",
  title =        "Should fixing these failures be delegated to automated
                 program repair?",
  year =         "2015",
  pages =        "427--437",
  abstract =     "Program repair constitutes one of the major components
                 of software maintenance that usually incurs a
                 significant cost in software production. Automated
                 program repair is supposed to help in reducing the
                 software maintenance cost by automatically fixing
                 software defects. Despite the recent advances in
                 automated software repair, it is still very costly to
                 wait for repair tools to produce valid repairs of
                 defects. This paper addresses the following question:
                 'Will an automated program repair technique find a
                 repair for a defect within a reasonable time?'. To
                 answer this question, we build an oracle that can
                 predict whether fixing a failure should be delegated to
                 an automated repair technique. If the repair technique
                 is predicted to take too long to produce a repair, the
                 bug fixing process should rather be assigned to a
                 developer or other appropriate techniques available.
                 Our oracle is built for genetic-programming-based
                 automated program repair approaches, which have
                 recently received considerable attention due to their
                 capability to automatically fix real-world bugs. These
                 approaches search for a valid repair over a large
                 number of variants that are syntactically mutated from
                 the original program. At an early stage of running a
                 repair tool, we extract a number of features that are
                 potentially related to the effectiveness of the tool.
                 Leveraging advances in machine learning, we process the
                 values of these features to learn a discriminative
                 model that is able to predict whether continuing a
                 genetic programming search will lead to a repair within
                 a desired time limit. We perform experiments to
                 evaluate the ability of our approach to predict the
                 effectiveness of GenProg, a well-known
                 genetic-programming-based automated program repair
                 approach, in fixing 105 real bugs. Our experiments show
                 that our approach can identify effective cases from
                 ineffective ones (i.e., bugs for which GenProg cannot
                 produce correct fixes after a long period of time) with
                 a precision, recall, F-measure, and AUC of 72percent,
                 74percent, 73percent, and 76percent respectively.",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  DOI =          "doi:10.1109/ISSRE.2015.7381836",
  month =        nov,
  notes =        "Also known as \cite{7381836}",

Genetic Programming entries for Xuan-Bach Dinh Le Tien-Duy Bui Le David Lo