Genetic Programming for Cross-Release Fault Count Predictions in Large and Complex Software Projects

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Wasif Afzal and Richard Torkar and Robert Feldt and 
                 Tony Gorschek",
  title =        "Genetic Programming for Cross-Release Fault Count
                 Predictions in Large and Complex Software Projects",
  booktitle =    "Evolutionary Computation and Optimization Algorithms
                 in Software Engineering: Applications and Techniques",
  publisher =    "IGI Global",
  year =         "2010",
  editor =       "Monica Chis",
  chapter =      "6",
  pages =        "94--126",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, SBSE",
  isbn13 =       "9781615208098",
  DOI =          "doi:10.4018/978-1-61520-809-8.ch006",
  abstract =     "Software fault prediction can play an important role
                 in ensuring software quality through efficient resource
                 allocation. This could, in turn, reduce the potentially
                 high consequential costs due to faults. Predicting
                 faults might be even more important with the emergence
                 of short-timed and multiple software releases aimed at
                 quick delivery of functionality. Previous research in
                 software fault prediction has indicated that there is a
                 need i) to improve the validity of results by having
                 comparisons among number of data sets from a variety of
                 software, ii) to use appropriate model evaluation
                 measures and iii) to use statistical testing
                 procedures. Moreover, cross-release prediction of
                 faults has not yet achieved sufficient attention in the
                 literature. In an attempt to address these concerns,
                 this paper compares the quantitative and qualitative
                 attributes of 7 traditional and machine-learning
                 techniques for modelling the cross-release prediction
                 of fault count data. The comparison is done using
                 extensive data sets gathered from a total of 7
                 multi-release open-source and industrial software
                 projects. These software projects together have several
                 years of development and are from diverse application
                 areas, ranging from a web browser to a robotic
                 controller software. Our quantitative analysis suggests
                 that genetic programming (GP) tends to have better
                 consistency in terms of goodness of fit and accuracy
                 across majority of data sets. It also has comparatively
                 less model bias. Qualitatively, ease of configuration
                 and complexity are less strong points for GP even
                 though it shows generality and gives transparent
                 models. Artificial neural networks did not perform as
                 well as expected while linear regression gave average
                 predictions in terms of goodness of fit and accuracy.
                 Support vector machine regression and traditional
                 software reliability growth models performed below
                 average on most of the quantitative evaluation criteria
                 while remained on average for most of the qualitative

Genetic Programming entries for Wasif Afzal Richard Torkar Robert Feldt Tony Gorschek