Software Defect Prediction on Unlabelled Datasets: A Comparative Study

Background: Defect prediction on unlabelled datasets is a challenging and widespread problem in software engineering. Machine learning is of great value in this context because it provides techniques called unsupervised - that are applicable to unlabelled datasets. Objective: This study aims at comparing various approaches employed over the years on unlabelled datasets to predict the defective modules, i.e. the ones which need more attention in the testing phase. Our comparison is based on the measurement of performance metrics and on the real defective information derived from software archives. Our work leverages a new dataset that has been obtained by extracting and preprocessing its metrics from a C++ software. Method: Our empirical study has taken advantage of CLAMI with its improvement CLAMI+ that we have applied on high energy physics software datasets. Furthermore, we have used clustering techniques such as the K-means algorithm to find potentially critical modules. Results: Our experimental analysis have been carried out on 1 open source project with 34 software releases. We have applied 17 ML techniques to the labelled datasets obtained by following the CLAMI and CLAMI+ approaches. The two approaches have been evaluated by using different performance metrics, our results show that CLAMI+ performs better than CLAMI. The predictive average accuracy metric is around 95% for 4 ML techniques (4 out of 17) that show a Kappa statistic greater than 0.80. We applied K-means on the same dataset and obtained 2 clusters labelled according to the output of CLAMI and CLAMI+. Conclusion: Based on the results of the different statistical tests, we conclude that no significant performance differences have been found in the selected classification techniques.