Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

Abstract

Maintaining large and complex software is a significant task in IT industry. One reason for that is the development of code smells which are design flaws that lead to future bugs and errors. Code smells can be treated with regular refactoring, and their detection is the first step in the software maintenance process. Detecting code smells with machine learning algorithms eliminate the need of extensive knowledge required regarding properties of code smell and threshold values. Ensemble machine learning algorithms use a combination of several same or different classifiers to further aid the performance and reduces the variance. In our study, three hybrid feature selection techniques with ensemble machine learning algorithms are employed to improve the performance in detecting code smells. Seven machine learning classifiers with different kernel variations, along with three boosting designs, two stacking methods, and bagging were implemented. For feature selection, combination of filter-wrapper, filter-embedded, and wrapper-embedded methods have been executed. Performance measures for detecting four code smells are evaluated and are compared with the performance when feature selection is not employed. It is found out that performance measure after application of hybrid feature selection increased, accuracy by 21.43%, AUC value by 53.24%, and f-measure by 76.06%. Univariate ROC with Lasso is the best hybrid feature selection technique with 90.48% accuracy and 94.5% ROC AUC value. Random Forest and Logistic regression are the best performing machine learning classifiers. Data class is most detectable code smell. Stacking always gave better results when compared with individual classifiers.