A Systematic Study of Automated Program Repair: Fixing 55 out of 105 bugs for \$8 Each

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Claire {Le Goues} and Michael Dewey-Vogt and 
                 Stephanie Forrest and Westley Weimer",
  title =        "A Systematic Study of Automated Program Repair: Fixing
                 55 out of 105 bugs for \$8 Each",
  booktitle =    "34th International Conference on Software Engineering
                 (ICSE 2012)",
  year =         "2012",
  editor =       "Martin Glinz",
  pages =        "3--13",
  address =      "Zurich",
  month =        jun # " 2-9",
  keywords =     "genetic algorithms, genetic programming, GenProg,
                 algorithmic improvement, automated program repair,
                 cloud computing resource, defect repair, grounded
                 human-competitive cost measurement, off-the-shelf C
                 program, open-source program, program bug, real-world
                 program, repair cost, systematic evaluation, C
                 language, cloud computing, program debugging, public
                 domain software, software cost estimation, software
  ISSN =         "0270-5257",
  URL =          "http://dijkstra.cs.virginia.edu/genprog/papers/weimer-icse2012-genprog-preprint.pdf",
  URL =          "https://squareslab.github.io/genprog-code/",
  DOI =          "doi:10.1109/ICSE.2012.6227211",
  size =         "11 pages",
  abstract =     "There are more bugs in real-world programs than human
                 programmers can realistically address. This paper
                 evaluates two research questions: What fraction of bugs
                 can be repaired automatically? and How much does it
                 cost to repair a bug automatically? In previous work,
                 we presented GenProg, which uses genetic programming to
                 repair defects in off-the-shelf C programs. To answer
                 these questions, we: (1) propose novel algorithmic
                 improvements to GenProg that allow it to scale to large
                 programs and find repairs 68percent more often, (2)
                 exploit GenProg's inherent parallelism using cloud
                 computing resources to provide grounded, human
                 competitive cost measurements, and (3) generate a
                 large, indicative benchmark set to use for systematic
                 evaluations. We evaluate GenProg on 105 defects from 8
                 open-source programs totalling 5.1 million lines of
                 code and involving 10,193 test cases. GenProg
                 automatically repairs 55 of those 105 defects. To our
                 knowledge, this evaluation is the largest available of
                 its kind, and is often two orders of magnitude larger
                 than previous work in terms of code or test suite size
                 or defect count. Public cloud computing prices allow
                 our 105 runs to be reproduced for 403 USA dollars; a
                 successful repair completes in 96 minutes and costs
                 $7.32, on average.",
  notes =        "GenProg >> ClearView, AutoFix-E, AFix.

                 Bug bounties, Tarsnap.

                 Chrome is ordered list of AST edit operations. Delete,
                 insert, swap, uniform crossover. 10 GP runs (population
                 40, <= ten generations, <12 hours) in parallel on
                 Amazon EC2 (c1.medium, 1.7Gbyte RAM) cloud. Less than
                 10percent of children fail to compile.

                 fbc, gmp, gzip, libtiff, lighttpd, php, Python,
                 wireshark approx 5.1 million lines of C code.

                 Human difficulty of bugfix != GP difficulty?

                 See also


                 Bronze winner 2012 HUMIES GECCO 2012.

                 cited by \cite{Nguyen:2013:ICSE}

                 Also known as \cite{6227211}",

Genetic Programming entries for Claire Le Goues Michael Dewey-Vogt Stephanie Forrest Westley Weimer