Software metrics and fault data belonging to a previous software version are used to build the software fault prediction model for the next release of the software. Until now, different classification algorithms have been used to build this kind of models. However there are certain cases when previous fault data are not present. In other words predicting the fault-proneness of program modules when the fault labels for modules are unavailable is a challenging task frequently arised in the software industry. Because fault data belonging to previous software version is not available, supervised learning approaches can not be applied, leading to the need for new methods, tools, or techniques. There is need to develop some methods to build the software fault prediction model based on unsupervised learning which can help to predict the fault –proneness of a program modules when fault labels for modules are not present. One of the such method is use of clustering techniques. This paper presents a case study of different clustering techniques and also compare the performance of these techniques.

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S/w Quality Clustering Various Clustering Approaches