Multiple Bug Spectral Fault Localization Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Lee Naish and Neelofar and Kotagiri Ramamohanarao",
  booktitle =    "24th Australasian Software Engineering Conference
  title =        "Multiple Bug Spectral Fault Localization Using Genetic
  year =         "2015",
  pages =        "11--17",
  abstract =     "Debugging is crucial for producing reliable software.
                 One of the effective bug localization techniques is
                 Spectral-Based Fault Localization (SBFL). It locates a
                 buggy statement by applying an evaluation metric to
                 program spectra and ranking program components on the
                 basis of the score it computes. Recently, genetic
                 programming has been proposed as a way to find good
                 metrics. We have found that the huge search space for
                 metrics can cause this approach to be slow and
                 unreliable, even for relatively simple data sets. Here
                 we propose a restricted class of 'hyperbolic' metrics,
                 with a small number of numeric parameters. This class
                 of functions is based on past theoretical and empirical
                 results. We show that genetic programming can reliably
                 discover effective metrics over a wide range of data
                 sets of program spectra. We evaluate the performance
                 for both real programs and model programs with single
                 bugs, multiple bugs, 'deterministic' bugs and
                 nondeterministic bugs.",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  DOI =          "doi:10.1109/ASWEC.2015.12",
  ISSN =         "1530-0803",
  month =        sep,
  notes =        "Also known as \cite{7365789}",

Genetic Programming entries for Lee Naish Neelofar Kotagiri Ramamohanarao