A Multi-Objective Software Quality Classification Model Using Genetic Programming

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

@Article{Khoshgoftaar:2007:ieeeTR,
  author =       "Taghi M. Khoshgoftaar and Yi Liu",
  title =        "A Multi-Objective Software Quality Classification
                 Model Using Genetic Programming",
  journal =      "IEEE Transactions on Reliability",
  year =         "2007",
  volume =       "56",
  number =       "2",
  pages =        "237--245",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, decision
                 trees, genetic algorithms, software metrics, software
                 quality, software reliability, genetic
                 programming-based decision tree model, multiobjective
                 software quality classification model, risk-based
                 software quality prediction, software fault-prone
                 module, software metrics, software quality assurance,
                 software quality-improvement, software reliability",
  DOI =          "doi:10.1109/TR.2007.896763",
  ISSN =         "0018-9529",
  abstract =     "A key factor in the success of a software project is
                 achieving the best-possible software reliability within
                 the allotted time & budget. Classification models which
                 provide a risk-based software quality prediction, such
                 as fault-prone & not fault-prone, are effective in
                 providing a focused software quality assurance
                 endeavor. However, their usefulness largely depends on
                 whether all the predicted fault-prone modules can be
                 inspected or improved by the allocated software
                 quality-improvement resources, and on the
                 project-specific costs of misclassifications.
                 Therefore, a practical goal of calibrating
                 classification models is to lower the expected cost of
                 misclassification while providing a cost-effective use
                 of the available software quality-improvement
                 resources. This paper presents a genetic
                 programming-based decision tree model which facilitates
                 a multi-objective optimization in the context of the
                 software quality classification problem. The first
                 objective is to minimize the Modified Expected Cost of
                 Misclassification, which is our recently proposed
                 goal-oriented measure for selecting & evaluating
                 classification models. The second objective is to
                 optimize the number of predicted fault-prone modules
                 such that it is equal to the number of modules which
                 can be inspected by the allocated resources. Some
                 commonly used classification techniques, such as
                 logistic regression, decision trees, and analogy-based
                 reasoning, are not suited for directly optimizing
                 multi-objective criteria. In contrast, genetic
                 programming is particularly suited for the
                 multi-objective optimization problem. An empirical case
                 study of a real-world industrial software system
                 demonstrates the promising results, and the usefulness
                 of the proposed model",
}

Genetic Programming entries for Taghi M Khoshgoftaar Yi Liu

Citations