Revisting the Impact of Regression Models for Predicting the Number of Defects

Predicting the number of faults in software modules can be more helpful instead of predicting the modules being faulty or non-faulty. Chen et al. (SEKE 397-402, 2015) and Rathore et al. (Soft Computing 21: 7417-7434, 2017) empirically investigate the feasibility of some regression algorithms for predicting the number of defects. The experimental results showed that the decision tree regression algorithm performed best in terms of average absolute error (AAE), average relative error (ARE) and root mean square error (RMSE). However, they did not consider the imbalanced data distribution problem in defect datasets and employed improper performance measures for evaluating the regression models to evaluate the performance of models for predicting the number of defects. Hence, we revisit the impact of different regression algorithms for predicting the number of defects using Fault-Percentile-Average (FPA) as the performance measure. The experiments on 31 datasets from PROMISE repository show that the prediction performance of models for predicting the number of defects built by different regression algorithms are various, and the gradient boosting regression algorithm and the Bayesian ridge regression algorithm can achieve better performance. Keywords—predicting the number of defects; regression algorithm; data imbalance; Fault-Percentile-Average;