Design Structural Stability Metrics and Post-Release Defect Density: An Empirical Study

This paper empirically explores the correlations between a suite of structural stability metrics for object-oriented designs and post-release defect density. The investigated stability metrics measure the extent to which the structure of a design is preserved throughout the evolution of the software from one release to the next. As a case study, thirteen successive releases of Apache Ant were analyzed. The results indicate that some of the stability metrics are significantly correlated with post-release defect density. It was possible to construct statistically significant regression models to estimate post-release defect density from subsets of these metrics. The results reveal the practical significance and usefulness of some of the investigated stability metrics as early indicators of one of the important software quality outcomes, which is post-release defect density

Design Structural Stability Metrics and Post-Release Defect Density: An Empirical Study

This paper empirically explores the correlations between a suite of structural stability metrics for object-oriented designs and post-release defect density. The investigated stability metrics measure the extent to which the structure of a design is preserved throughout the evolution of the software from one release to the next. As a case study, thirteen successive releases of Apache Ant were analyzed. The results indicate that some of the stability metrics are significantly correlated with post-release defect density. It was possible to construct statistically significant regression models to estimate post-release defect density from subsets of these metrics. The results reveal the practical significance and usefulness of some of the investigated stability metrics as early indicators of one of the important software quality outcomes, which is post-release defect density

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio