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 indicate 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.

本文提出了TACO（代码异味检测文本分析，Textual Analysis for Code Smell Detection），这是一种依托文本分析技术，可检测多种性质、不同粒度层级的代码异味家族的方法。我们在10个开源项目上开展TACO的验证实验，并将其性能与仅基于代码组件提取的结构信息的现有异味检测器进行对比。结果分析表明，TACO的精确率区间为67%至77%，召回率区间为72%至84%。此外，TACO在多数场景下优于其他结构型检测方法，再次验证了从代码组件文本部分可提取的信息的实用价值。