Evolving Human Competitive Spectra-Based Fault Localisation Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@TechReport{rn-12-03,
  author =       "Shin Yoo",
  title =        "Evolving Human Competitive Spectra-Based Fault
                 Localisation Techniques",
  institution =  "Department of Computer Science, University College,
                 London",
  year =         "2012",
  type =         "Research Note",
  number =       "RN/12/03",
  address =      "UK",
  month =        "8 " # may,
  keywords =     "genetic algorithms, genetic programming, SBSE, GPU,
                 openCL",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.5356",
  URL =          "http://www-typo3.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/rn-12-03.pdf",
  size =         "13 pages",
  abstract =     "Spectra-Based Fault Localisation (SBFL) aims to assist
                 de- bugging by applying risk evaluation formulae
                 (sometimes called suspiciousness metrics) to program
                 spectra and ranking statements according to the
                 predicted risk. Designing a risk evaluation formula is
                 often an intuitive process done by human software
                 engineer. This paper presents a Genetic Programming
                 approach for evolving risk assessment formulae. The
                 empirical evaluation using 92 faults from four Unix
                 utilities produces promising results. GP-evolved
                 equations can consistently outperform many of the
                 human-designed formulae, such as Tarantula, Ochiai,
                 Jaccard, Ample, and Wong1/2, up to 5.9 times. More
                 importantly, they can perform equally as well as Op2,
                 which was recently proved to be optimal against
                 If-Then-Else-2 (ITE2) structure, or even outperform it
                 against other program structures.",
  notes =        "The program spectra data used in the paper, as well as
                 the complete empirical results, are available from:
                 http://www.cs.ucl.ac.uk/staff/s.yoo/evolving-sbfl.html

                 SIR (flex, grep, gzip, sed), gcov, Linux, pyevolve
                 (python).

                 Published as \cite{Yoo:2012:SSBSE}

                 Entered 2012 HUMIES GECCO 2012.
                 http://www.genetic-programming.org/combined.php",
}

Genetic Programming entries for Shin Yoo

Citations