Search-based Prediction of Fault-slip-through in Large Software Projects

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Afzal:2010:SSBSE,
  author =       "Wasif Afzal and Richard Torkar and Robert Feldt and 
                 Greger Wikstrand",
  title =        "Search-based Prediction of Fault-slip-through in Large
                 Software Projects",
  booktitle =    "Second International Symposium on Search Based
                 Software Engineering (SSBSE 2010)",
  year =         "2010",
  month =        "7-9 " # sep,
  pages =        "79--88",
  address =      "Benevento, Italy",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, sbse, AIRS, GEP, GP, MR,
                 PSO-ANN, artificial immune recognition system,
                 artificial neural network, fault-slip-through, multiple
                 regression, particle swarm optimisation, search-based
                 prediction, software project, software testing process,
                 artificial immune systems, fault tolerant computing,
                 neural nets, particle swarm optimisation, program
                 testing, regression analysis",
  DOI =          "doi:10.1109/SSBSE.2010.19",
  isbn13 =       "978-0-7695-4195-2",
  abstract =     "A large percentage of the cost of rework can be
                 avoided by finding more faults earlier in a software
                 testing process. Therefore, determination of which
                 software testing phases to focus improvements work on,
                 has considerable industrial interest. This paper
                 evaluates the use of five different techniques, namely
                 particle swarm optimization based artificial neural
                 networks (PSO-ANN), artificial immune recognition
                 systems (AIRS), gene expression programming (GEP),
                 genetic programming (GP) and multiple regression (MR),
                 for predicting the number of faults slipping through
                 unit, function, integration and system testing phases.
                 The objective is to quantify improvement potential in
                 different testing phases by striving towards finding
                 the right faults in the right phase. We have conducted
                 an empirical study of two large projects from a
                 telecommunication company developing mobile platforms
                 and wireless semiconductors. The results are compared
                 using simple residuals, goodness of fit and absolute
                 relative error measures. They indicate that the four
                 search-based techniques (PSO-ANN, AIRS, GEP, GP)
                 perform better than multiple regression for predicting
                 the fault-slip-through for each of the four testing
                 phases. At the unit and function testing phases, AIRS
                 and PSO-ANN performed better while GP performed better
                 at integration and system testing phases. The study
                 concludes that a variety of search-based techniques are
                 applicable for predicting the improvement potential in
                 different testing phases with GP showing more
                 consistent performance across two of the four test
                 phases.",
  notes =        "IEEE Computer Society Order Number P4195 BMS Part
                 Number: CFP1099G-PRT Library of Congress Number
                 2010933544 http://ssbse.org/2010/program.php Also known
                 as \cite{5635180}",
}

Genetic Programming entries for Wasif Afzal Richard Torkar Robert Feldt Greger Wikstrand

Citations