Improving Change Prediction Models with Code Smell-Related Information

Code smells are sub-optimal implementation choices applied by developers that have the effect of negatively impacting, among others, the change-proneness of the affected classes. Based on this consideration, in this paper we conjecture that code smell-related information can be effectively exploited to improve the performance of change prediction models, i.e., models having the goal of indicating which classes are more likely to change in the future. We exploit the so-called intensity index—a previously defined metric that captures the severity of a code smell—and evaluate its contribution when added as additional feature in the context of three state of the art change prediction models based on product, process, and developer-based features. We also compare the performance achieved by the proposed model with a model based on previously defined antipattern metrics, a set of indicators computed considering the history of code smells in files. Our results report that (i) the prediction performance of the intensity-including models is statistically better than the baselines and, (ii) the intensity is a better predictor than antipattern metrics. We observed some orthogonality between the set of change-prone and non-change-prone classes correctly classified by the models relying on intensity and antipattern metrics: for this reason, we also devise and evaluate a smell-aware combined change prediction model including product, process, developer-based, and smell-related features. We show that the F-Measure of this model is notably higher than other models.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin