Software quality assessment using a multi-strategy classifier

Classifying program modules as fault-prone or not fault-prone is a valuable technique for guiding the software development process, so that resources can be allocated to components most likely to have faults. The rule-based classification and the case-based learning techniques are commonly used in software quality classification problems. However, studies show that these two techniques share some complementary strengths and weaknesses. Therefore, in this paper we propose a new multi-strategy classification model, RB2CBL, which integrates a rule-based (RB) model with two case-based learning (CBL) models. RB2CBL possesses the merits of both the RB model and CBL model and restrains their drawbacks. In the RB2CBL model, the parameter optimization of the CBL models is critical and an embedded genetic algorithm optimizer is used. Two case studies were carried out to validate the proposed method. The results show that, by suitably choosing the accuracy of the RB model, the RB2CBL model outperforms the RB model alone without overfitting.