Software quality classification using rule-based modeling

Software-based products are part of our daily life. They can be encountered in most of the systems we interact with. This reliance on software products generates a strong need for better software reliability, reducing the cost associated with potential failures. Reliability in software systems may be achieved by using additional testing. However, extensive software testing is expensive and time consuming. Software quality classification models provide an early prediction of a module's quality. Boolean Discriminant Function (BDF), Generalized Boolean Discriminant Function (GBDF), and Rule-Based Modeling (RBM) can be used as classification models. This thesis demonstrates the ability of GBDF and RBM to correctly classify modules. The introduction of the AND operator in the GBDF model and the customizable outcomes for the rules in RBM, enhanced the discriminating quality of GBDF and RBM as compared to BDF. Furthermore, they also yielded better balances for the misclassification rates.