Abstract

Software plays a major role in many organizations. Organizational success depends partially on the quality of software used. In recent years, many researchers have recognized that statistical classification techniques are well-suited to develop software quality prediction models. Different statistical software quality models, using complexity metrics as early indicators of software quality, have been proposed in the past. At a high-level the problem of software categorization is to classify software modules into fault prone and non-fault prone. The focus of this thesis is two-fold. One is to study some selected classification techniques including unsupervised and supervised learning algorithms widely used for software categorization. The second emphasis is to explore a new unsupervised learning model, employing Bayesian and deterministic approaches. Besides, we evaluate and compare experimentally these approaches using a real data set. Our experimental results show that different algorithms lead to different statistically significant results.