A Comparative Study of Unsupervised Learning Algorithms for Software Fault Prediction

Software fault prediction is an important task in software development process which enables software practitioners to easily detect and rectify the errors in modules or classes. Various fault prediction techniques have been studied in the past and unsupervised learning methods such as clustering techniques are drawing much attention in the recent years. K-means is a well known clustering algorithm which is applied on various exploratory analysis including software fault prediction. This paper provides a comparative study on software fault prediction using K-means clustering algorithm and its variants. We use five software fault prediction datasets taken from PROMISE repository to evaluate the prediction accuracy of the clustering algorithms. Experimental results indicate that proper initial seed selection enables K-means algorithm to effectively group the faulty modules.