Predicting Post-Release Software Faults In Open Source Software As a Means Of Measuring Intrinsic Software Product Quality

Abstract

Faulty software have expensive consequences. To mitigate these cousequences, soft­ ware developers have to identify and fix faulty software components before releasing software products. Similarly, users have to gauge the delivered quality of software be­ fore adoption of the software. However the abstract nature of software and the multiple dimensions of software quality impede organizations from measuring software quality. Software quality metrics can be used as proxies of software quality to ease the com­ plexity of measuring reliability dimension of software quality. Previous studies have suggested that software process metrics are better predictors of software faults as com­ pared to software product metrics. However, there is need for a specific software process metric that can guarantee consistent superior fault prediction performances across dif­ ferent contexts. This research sought to determine a predictor for software faults that has the best prediction performance, requires least effort to detect software faults, and has a minimum cost of misclassifyiug components. In addition, the study investigated the effect of combining predictors on the performance of software fault prediction mo­ dels. Experimental data sets for this study were derived from four heterogeneous Open Source Software projects. Logistic Regression algorithm was used to predict bug status of each file, while Linear Regression algorithm was used to predict number of bugs per file. Prediction performance of the models built with software metrics as predictors was evaluated against numerical model performance measures, effort of prediction, and cost of misclassification of components. Models built. with Change Durst metrics registered overall best performance as compared to those built with Chango, Cede Churn, Deve loper Networks and Source Code software metrics. Change Durst metrics recorded the highest values for numerical performance measures, exhibited the highest fault detec­ tion probabilities ranging between 68% to 55% upon examination of only 20% of source code, and had the least cost of misclassification of components. Combining software me­ trics was found not to significantly improve performance of software faults prediction models. The study concluded that the Change Burst metrics model could effectively pre­ dict software faults. Random Forest's IncNodePurity revealed that six Change Durst metrics were most influential in predicting software faults. This study recommended that the six Change Durst metrics should be used when predicting software faults.