Comparison of Machine Learning Methods for Code Smell Detection Using Reduced Features

We examine a machine learning approach for detecting common Class and Method level code smells (Data Class and God Class, Feature Envy and Long Method). The focus of the work is selection of reduced set of features that will achieve high classification accuracy. The proposed features may be used by the developers to develop better quality software since the selected features focus on the most critical parts of the code that is responsible for creation of common code smells. We obtained a high accuracy results for all four code smells using the selected features: 98.57% for Data Class, 97.86% for God Class, 99.67% for Feature Envy, and 99.76% for Long Method.