A preliminary study on the adequacy of static analysis warnings with respect to code smell prediction

Code smells are poor implementation choices applied during software evolution that can affect source code maintainability. While several heuristic-based approaches have been proposed in the past, machine learning solutions have recently gained attention since they may potentially address some limitations of state-of-the-art approaches. Unfortunately, however, machine learning-based code smell detectors still suffer from low accuracy. In this paper, we aim at advancing the knowledge in the field by investigating the role of static analysis warnings as features of machine learning models for the detection of three code smell types. We first verify the potential contribution given by these features. Then, we build code smell prediction models exploiting the most relevant features coming from the first analysis. The main finding of the study reports that the warnings given by the considered tools lead the performance of code smell prediction models to drastically increase with respect to what reported by previous research in the field.