Feature reduction techniques based code smell prediction

Abstract

Code Smell refers to the telltale signs of poor code design that leads to software quality issues. Developers require specific methods to measure the complexity of Code Smells in order to resolve the problem quickly. Recent research has examined the problem of predicting Code Smell using various detection methods. However, the accuracy of machine learning-based Code Smell detectors is still at a normal level. One of the main objective of this paper is to assess how well dimensionality reduction methods can predict Code Smells. This paper uses three machine learning techniques with feature reduction techniques, such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminate Analysis (LDA). Ten-fold cross-validation is used to ensure that the model is well-trained. Datasets are balanced using the Synthetic Minority Oversampling Technique (SMOTE) to ensure an equal number of classes in each dataset. The experimental result concluded that the AdaBoost method with LDA performs better in both the Long Parameter List and Switch Statement datasets, with an accuracy of 92.72% and 91.24%, respectively.