Evolutionary Optimization of Software Quality Modeling with Multiple Repositories

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Liu:2010:ieeeTSE,
  author =       "Yi (Cathy) Liu and Taghi M. Khoshgoftaar and 
                 Naeem Seliya",
  title =        "Evolutionary Optimization of Software Quality Modeling
                 with Multiple Repositories",
  journal =      "IEEE Transactions on Software Engineering",
  year =         "2010",
  month =        nov # "/" # dec,
  volume =       "36",
  number =       "6",
  pages =        "852--864",
  ISSN =         "0098-5589",
  abstract =     "A novel search-based approach to software quality
                 modelling with multiple software project repositories
                 is presented. Training a software quality model with
                 only one software measurement and defect data set may
                 not effectively encapsulate quality trends of the
                 development organisation. The inclusion of additional
                 software projects during the training process can
                 provide a cross-project perspective on software quality
                 modelling and prediction. The genetic-programming-based
                 approach includes three strategies for modeling with
                 multiple software projects: Baseline Classifier,
                 Validation Classifier, and Validation-and-Voting
                 Classifier. The latter is shown to provide better
                 generalisation and more robust software quality models.
                 This is based on a case study of software metrics and
                 defect data from seven real-world systems. A second
                 case study considers 17 different (nonevolutionary)
                 machine learners for modelling with multiple software
                 data sets. Both case studies use a similar
                 majority-voting approach for predicting fault-proneness
                 class of program modules. It is shown that the total
                 cost of misclassification of the search-based software
                 quality models is consistently lower than those of the
                 non-search-based models. This study provides clear
                 guidance to practitioners interested in exploiting
                 their organization's software measurement data
                 repositories for improved software quality modelling.",
  keywords =     "genetic algorithms, genetic programming, sbse,
                 baseline classifier, evolutionary optimisation, machine
                 learner, multiple software project repository, robust
                 software quality model, search-based software quality
                 model, software data set, software measurement data
                 repository, software metrics, software quality
                 modelling, validation classifier, validation-and-voting
                 classifier, software management, software metrics,
                 software quality",
  DOI =          "doi:10.1109/TSE.2010.51",
  ISSN =         "0098-5589",
  notes =        "Also known as \cite{5467094}",
}

Genetic Programming entries for Yi Liu Taghi M Khoshgoftaar Jim Seliya

Citations