Local and Global Feature Based Explainable Feature Envy Detection
Xin Yin, Chongyang Shi, Shuxin Zhao · 2021
Code smell detection can help developers identify position of code smell in projects and enhance the quality of software system. Usually codes with similar semantic relationships have greater code dependencies, and most code smell detection methods ignore dependencies relationships within the source code. Thus, their detection results may be heavily influenced by inadequate code feature, which can lead to some code smell not being detected. In addition, existing methods cannot explain the correlation between detection results and code information. However, an explainable result can help developers make better judgments on code smell reconstruction. Accordingly, in this paper, we propose a local and global feature based explainable approach to detecting feature envy, one of the most common code smells. For the model to make the most of code information, we design different representation models for global code and local code respectively to extract different feature envy features, and automatically combine these features that are beneficial in terms of detection accuracy. We further design a code semantic dependency (CSD) to make the detection result easy to explain. The evaluation results of seven manual building code smell projects and three real projects show that the proposed approach improves on the state-of-the-art in detecting feature envy and boosting the explainability of results.