Search-Based Prediction of Fault Count Data
by W. Afzal and R. Torkar and R. Feldt
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-deļ¬ned 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.


  author =    "Wasif Afzal and Richard Torkar and Robert Feldt",
  title =     "Search-Based Prediction of Fault Count Data",
  year =      "2009",
  editor =    "Massimiliano {Di Penta} and Simon Poulding",
  booktitle = "Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009",
  month =     "May 13-15",
  address =   "Windsor, UK",
  publisher = "IEEE",
  isbn =      "978-0-7695-3675-0",
  keywords =  "Genetic algorithms; Genetic programming; Search-Based Software Engineering",
  url =       "",
  url =       "",