Genetic Programming-Based Decision Trees for Software Quality Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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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