Code Bad Smell Detection through Evolutionary Data Mining

Abstract

The existence of code bad smell has a severe impact on the software quality. Numerous researches show that ignoring code bad smells can lead to failure of a software system. Thus, the detection of bad smells has drawn the attention of many researchers and practitioners. Quite a few approaches have been proposed to detect code bad smells. Most approaches are solely based on structural information extracted from source code. However, we have observed that some code bad smells have the evolutionary property, and thus propose a novel approach to detect three code bad smells by mining software evolutionary data: duplicated code, shotgun surgery, and divergent change. It exploits association rules mined from change history of software systems, upon which we define heuristic algorithms to detect the three bad smells. The experimental results on five open source projects demonstrate that the proposed approach achieves higher precision, recall and F-measure.