Software Fault Prediction of Unlabeled Program Modules

Abstract

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 cases when previous fault data are not present; and hence, supervised learning approaches cannot be applied. In this study, we propose a fully automated technique which does not require an expert during the prediction process. In addition, it is not required to identify the number of clusters before the clustering phase, as required by K-means clustering method. Software metrics thresholds are used to remove the expert necessity. Our technique first applies X-means clustering method to cluster modules and identifies the best cluster number. After this step, the mean vector of each cluster is checked against the metrics thresholds vector. A cluster is predicted as fault-prone if at least one metric of the mean vector is higher than the threshold value of that metric. In addition to X-means clustering-based method, we made experiments with pure metrics thresholds method, fuzzy clustering, and K-means clustering-based methods. Experiments reveal that unsupervised software fault prediction can be fully automated and effective results can be produced using X-means clustering with software metrics thresholds. Three datasets, collected from Turkish white-goods manufacturer developing embedded controller software, have been used for the validation.