Evolutionary Optimization of Software Quality Modeling with Multiple Repositories

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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",
• 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",
• ISSN = "0098-5589",
• DOI = "doi:10.1109/TSE.2010.51",
• 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.",
• notes = "Also known as \cite{5467094}",

}

Genetic Programming entries for Yi Liu Taghi M Khoshgoftaar Jim Seliya

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