Modeling Software Defects as Anomalies: A Case Study on Promise Repository

Abstract

Software defect prediction is a highly studied domain in Software Engineering research due to its importance in software development. In literature, various classification methods with static code attributes have been used to predict defects. However, defected instances are very few compared to non-defected instances and as such lead to imbalanced data. Traditional machine learning techniques give poor results for such data. In this paper an anomaly detection technique for software defect prediction, is proposed which is not affected by imbalanced data. The technique incorporates both univariate and multivariate Gaussian distribution to model non-defected software module. The defected software modules are then predicted based on their deviation from the generated model. To evaluate our approach, we implemented the algorithm and tested it on the NASA datasets from the PROMISE repository. By utilizing this approach, we observed an average balance of 63.36% and 69.06% in univariate model and multivariate model respectively. Without utilizing optimization or filter, this approach yield better result than industry standard of 60%.