Evolution vs. Intelligent Design in Program Patching

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

  author =       "Yuriy Brun and Earl Barr and Ming Xiao and 
                 Claire {Le Goues} and P. Devanbu",
  title =        "Evolution vs. Intelligent Design in Program Patching",
  institution =  "Dept. of Computer Science, University of California,
  year =         "2013",
  address =      "USA",
  month =        "Fall",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  URL =          "https://escholarship.org/uc/item/3z8926ks.pdf",
  size =         "13 pages",
  abstract =     "While Fixing bugs requires significant manual effort,
                 recent research has shown that genetic programming (GP)
                 can be used to search through a space of programs to
                 automatically Find candidate bugfixing patches. Given a
                 program, and a set of test cases (some of which fail),
                 a GP-based repair technique evolves a patch or a
                 patched program using program mutation and selection
                 operators. We evaluate GenProg, a well-known GP-based
                 patch generator, using a large, diverse dataset of over
                 a thousand simple (both buggy and correct)
                 student-written homework programs, using two different
                 test sets: a white-box test set constructed to achieve
                 edge coverage on an oracle program, and a black-box
                 test set developed to exercise the desired
                 specification. We Find that GenProg often succeeds at
                 Finding a patch that will cause student programs to
                 pass supplied white-box test cases; however, that the
                 solution quite often overfits to the supplied tests and
                 doesn't pass all the black-box tests. In contrast, when
                 students patch their own buggy programs, these patches
                 tend to pass the black-box tests as well. We also Find
                 that the GenProg-generated patches lack enough
                 diversity to benefit from a kind of bagging, in which a
                 plurality vote over a population of GP-generated
                 patches outperforms a randomly chosen individual patch.
                 We report these results and additional relationships
                 between GenProg's success and the size and complexity
                 of the manual and automatic patches.",
  notes =        "cited by \cite{Smith:2015:FSE}",

Genetic Programming entries for Yuriy Brun Earl Barr Ming Xiao Claire Le Goues Premkumar Devanbu