A textual-based technique for Smell Detection

In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect a family of smells of different nature and different levels of granularity. We run TACO on 10 open source projects, comparing its performance with existing smell detectors purely based on structural information extracted from code components. The analysis of the results indicates that TACO's precision ranges between 67% and 77%, while its recall ranges between 72% and 84%. Also, TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of code components.