Building Decision Tree Software Quality Classification Models Using Genetic Programming

Abstract

Predicting the quality of software modules prior to testing or system operations allows a focused software quality improvement endeavor. Decision trees are very attractive for classification problems, because of their comprehensibility and white box modeling features. However, optimizing the classification accuracy and the tree size is a difficult problem, and to our knowledge very few studies have addressed the issue. This paper presents an automated and simplified genetic programming (GP) based decision tree modeling technique for calibrating software quality classification models. The proposed technique is based on multi-objective optimization using strongly typed GP. Two fitness functions are used to optimize the classification accuracy and tree size of the classification models calibrated for a real-world high-assurance software system. The performances of the classification models are compared with those obtained by standard GP. It is shown that the gp-based decision tree technique yielded better classification models.