A Textual-based Technique for Code and Test Smell Detection

Bad smells are symptoms of poor design or imple- mentation choices that may affect both production and test code. Previous studies demonstrated the huge impact of these smells on change- and fault-proneness, comprehensibility and, more in general, on maintainability. For these reasons researchers have spent a lot of effort on the definition of methods and tools aimed at detecting smells in production and test code. Existing detection techniques are largely based on using structural information extracted from source code without considering the textual content of the code, which can be a useful source of information to detect design flaws. In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect smells in both production code and test code. We run TACO on 20 open source projects, comparing its performance with existing state-of-the-art smell detectors purely based on structural analysis. Our findings indicate that TACO’s precision ranges between 57% and 75%, while its recall ranges between 68% and 85%. Moreover, we observed that TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of source code components.