Software metrics data clustering for quality prediction

Software metrics are collected at various phases of the software development process. These metrics contain the information of software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which is one of data mining techniques, is adopted to build the software quality prediction models in early period of software testing. In this paper, three clustering methods, k-means, fuzzy c-means and Gaussian mixture model, are investigated for the analysis of two real-world software metric datasets. The experiment results show that the best method in predicting software quality is dependent on practical dataset, and clustering analysis technique has advantages in software quality prediction since it can be used in the case having little prior knowledge.