A COMPARATIVE STUDY OF FILTER-BASED AND WRAPPER-BASED FEATURE RANKING TECHNIQUES FOR SOFTWARE QUALITY MODELING

Data mining techniques have been effectively used for software defect prediction in the last decade. The general process is that a classifier is first trained on historical software data (software metrics and fault data) collected during the software development process and then the classifier is used to predict new program modules (waiting for testing) as either fault-prone or not-fault-prone. The performance of the classifier is influenced by two factors: the software metrics in the training dataset and the proportions of the fault-prone and not-fault-prone modules in that dataset. When a dataset contains too many software metrics and/or very skewed proportions of the two types of modules, several problems may arise including extensive computation and a decline in predictive performance. In this paper, we use feature ranking and data sampling to deal with these problems. We investigate two types of feature ranking techniques (wrapper-based and filter-based), and compare their performances through two case studies on two groups of software measurement datasets. The empirical results demonstrate that filter-based ranking techniques not only show better classification performance but also have a lower computational cost.