Feature selection

Pattern recognition (psychology)

Maintainability

Test set

Feature (linguistics)

Naive Bayes classifier

Software development

Operating system

Support vector machine

Data mining

Code smell

Software engineering

Software quality

Machine learning

Philosophy

Artificial intelligence

Set (abstract data type)

Programming language

Class (philosophy)

Code (set theory)

Software

Computer science

Linguistics

Source code

IEEE Access

Code smells detection helps in improving understandability and maintainability of software while reducing the chances of system failure. In this study, six machine learning algorithms have been applied to predict code smells. For this purpose, four code smell datasets (God-class, Data-class, Feature-envy, and Long-method) are considered which are generated from 74 open-source systems. To evaluate the performance of machine learning algorithms on these code smell datasets, 10-fold cross validation technique is applied that predicts the model by partitioning the original dataset into a training set to train the model and test set to evaluate it. Two feature selection techniques are applied to enhance our prediction accuracy. The Chi-squared and Wrapper-based feature selection techniques are used to improve the accuracy of total six machine learning methods by choosing the top metrics in each dataset. Results obtained by applying these two feature selection techniques are compared. To improve the accuracy of these algorithms, grid search-based parameter optimization technique is applied. In this study, 100% accuracy was obtained for the Long-method dataset by using the Logistic Regression algorithm with all features while the worst performance 95.20 % was obtained by Naive Bayes algorithm for the Long-method dataset using the chi-square feature selection technique.

A Novel Approach for Code Smell Detection: An Empirical Study