Designing better fitness functions for automated program repair

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Fast:2010:GECCO,
  author =       "Ethan Fast and Claire {Le Goues} and 
                 Stephanie Forrest and Westley Weimer",
  title =        "Designing better fitness functions for automated
                 program repair",
  year =         "2010",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "965--972",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Software repair, software engineering",
  URL =          "http://www.cs.virginia.edu/~weimer/p/weimer-gecco2010-preprint.pdf",
  DOI =          "doi:10.1145/1830483.1830654",
  size =         "8 pages",
  abstract =     "Evolutionary methods have been used to repair programs
                 automatically, with promising results. However, the
                 fitness function used to achieve these results was
                 based on a few simple test cases and is likely too
                 simplistic for larger programs and more complex bugs.
                 We focus here on two aspects of fitness evaluation:
                 efficiency and precision. Efficiency is an issue
                 because many programs have hundreds of test cases, and
                 it is costly to run each test on every individual in
                 the population. Moreover, the precision of fitness
                 functions based on test cases is limited by the fact
                 that a program either passes a test case, or does not,
                 which leads to a fitness function that can take on only
                 a few distinct values. This paper investigates two
                 approaches to enhancing fitness functions for program
                 repair, incorporating (1) test suite selection to
                 improve efficiency and (2) formal specifications to
                 improve precision. We evaluate test suite selection on
                 10 programs, improving running time for automated
                 repair by 81percent. We evaluate program invariants
                 using the Fitness Distance Correlation (FDC) metric,
                 demonstrating significant improvements and smoother
                 evolution of repairs.",
  notes =        "deroff, gcd, look, uniq, and zune nullhttpd, lighttpd,
                 zune, tiff, leukocyte, and imagemagick. SUS. Oracle
                 comparator, sand box, diffX. Daikon, (cites ClearView,
                 Chianti.) pop=40.

                 Also known as \cite{1830654} GECCO-2010 A joint meeting
                 of the nineteenth international conference on genetic
                 algorithms (ICGA-2010) and the fifteenth annual genetic
                 programming conference (GP-2010)",
}

Genetic Programming entries for Ethan Joseph Fast Claire Le Goues Stephanie Forrest Westley Weimer

Citations