Genetic Programming-Based Decision Trees for Software Quality Classification

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

@InProceedings{Khoshgoftaar03,
  author =       "Taghi M. Khoshgoftaar and Yi Liu and Naeem Seliya",
  title =        "Genetic {P}rogramming-{B}ased {D}ecision {T}rees for
                 {S}oftware {Q}uality {C}lassification",
  booktitle =    "Proceedings of the Fifteenth International Conference
                 on Tools with Artificial Intelligence (ICTAI 03)",
  pages =        "374--383",
  publisher =    "IEEE Computer Society",
  address =      "Los Alamitos, California",
  month =        "3-5 " # nov,
  year =         "2003",
  keywords =     "genetic algorithms, genetic programming, decision
                 trees, program testing, software metrics, software
                 quality, C4.5 decision tree, GP-based decision trees,
                 S-expression tree, automated genetic programming,
                 classification model, misclassification cost,
                 multiobjective optimization, multiple criteria, program
                 module, risk-based classes, simultaneous optimization,
                 software development, software inspection, software
                 metrics, software quality classification, software
                 system, software testing, tree-structure",
  ISSN =         "1082-3409",
  DOI =          "doi:10.1109/TAI.2003.1250214",
  size =         "10 pages",
  abstract =     "The knowledge of the likely problematic areas of a
                 software system is very useful for improving its
                 overall quality. Based on such information, a more
                 focused software testing and inspection plan can be
                 devised. Decision trees are attractive for a software
                 quality classification problem which predicts the
                 quality of program modules in terms of risk-based
                 classes. They provide a comprehensible classification
                 model which can be directly interpreted by observing
                 the tree-structure. A simultaneous optimisation of the
                 classification accuracy and the size of the decision
                 tree is a difficult problem, and very few studies have
                 addressed the issue. This paper presents an automated
                 and simplified genetic programming (gp) based decision
                 tree modelling technique for the software quality
                 classification problem. Genetic programming is ideally
                 suited for problems that require optimisation of
                 multiple criteria. The proposed technique is based on
                 multi-objective optimisation using strongly typed GP.
                 In the context of an industrial high-assurance software
                 system, two fitness functions are used for the
                 optimization problem: one for minimising the average
                 weighted cost of misclassification, and one for
                 controlling the size of the decision tree. The
                 classification performances of the GP-based decision
                 trees are compared with those based on standard GP,
                 i.e., S-expression tree. It is shown that the GP-based
                 decision tree technique yielded better classification
                 models. As compared to other decision tree-based
                 methods, such as C4.5, GP-based decision trees are more
                 flexible and can allow optimisation of performance
                 objectives other than accuracy. Moreover, it provides a
                 practical solution for building models in the presence
                 of conflicting objectives, which is commonly observed
                 in software development practice.",
  notes =        "Inspec Accession Number: 7862146",
}

Genetic Programming entries for Taghi M Khoshgoftaar Yi Liu Jim Seliya

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