Extracting classification rule of software diagnosis using modified MEPA
Expert Systems with Applications: An International Journal
Software quality estimation with limited fault data: a semi-supervised learning perspective
Software Quality Control
Fuzzy Decision Tree Induction Approach for Mining Fuzzy Association Rules
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
A novel composite model approach to improve software quality prediction
Information and Software Technology
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Reducing overfitting in genetic programming models for software quality classification
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Combining techniques for software quality classification: An integrated decision network approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
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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 focussed 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 thetree-structure. A simultaneous optimization 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 modeling technique for the software quality classification problem. Genetic programming is ideally suited for problems that require optimization of multiple criteria. The proposed technique is based on multi-objective optimization 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 minimizing 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 optimization 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