Quality prediction model of object-oriented software system using computational intelligence

Abstract

Effective prediction of the fault-proneness plays a very important role in the analysis of software quality and balance of software cost, and it also is an important problem of software engineering. Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. In this paper, we use fuzzy c-means clustering (FCM) and radial basis function neural network (RBFNN) to construct prediction model of the fault-proneness, RBFNN is used as a classificatory, and FCM is as a cluster. Object-oriented software metrics are as input variables of fault prediction model. Experiments results confirm that designed model is very effective for predicting a class's fault-proneness, it has a high accuracy, and its implementation requires neither extra cost nor expert's knowledge. It also is automated. Therefore, proposed model was very useful in predicting software quality and classing the fault-proneness.