Search-Based Prediction of Fault Count Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Wasif Afzal and Richard Torkar and Robert Feldt",
  title =        "Search-Based Prediction of Fault Count Data",
  booktitle =    "Proceedings 1st International Symposium on Search
                 Based Software Engineering SSBSE 2009",
  year =         "2009",
  editor =       "Massimiliano {Di Penta} and Simon Poulding",
  pages =        "35--38",
  address =      "Windsor, UK",
  month =        "13-15 " # may,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 search-based prediction, software fault count data,
                 software reliability growth model, symbolic regression,
                 regression analysis, software fault tolerance",
  isbn13 =       "978-0-7695-3675-0",
  DOI =          "doi:10.1109/SSBSE.2009.17",
  abstract =     "Symbolic regression, an application domain of genetic
                 programming (GP), aims to find a function whose output
                 has some desired property, like matching target values
                 of a particular data set. While typical regression
                 involves finding the coefficients of a pre-defined
                 function, symbolic regression finds a general function,
                 with coefficients, fitting the given set of data
                 points. The concepts of symbolic regression using
                 genetic programming can be used to evolve a model for
                 fault count predictions. Such a model has the
                 advantages that the evolution is not dependent on a
                 particular structure of the model and is also
                 independent of any assumptions, which are common in
                 traditional time-domain parametric software reliability
                 growth models. This research aims at applying
                 experiments targeting fault predictions using genetic
                 programming and comparing the results with traditional
                 approaches to compare efficiency gains.",
  notes =        "order number P3675 Also known as

Genetic Programming entries for Wasif Afzal Richard Torkar Robert Feldt