CODE SMELLS-BASED FAULT PREDICTION USING DEEP LEARNING TECHNIQUE

Abstract

Code smells are the structural characteristics of the software under development that indicate poor design or code choices that have the potential to cause an error or failure in the software such that it makes the software difficult to evolve and maintain. The objective of this study is to assure the quality of the software by predicting the probability of the existence of faults by considering software metrics, code smells and code smell metrics in the software. We consider three types of code smells-based datasets that include code smells only, code smells and metrics, or code smell metrics, and metrics. We label the unlabeled datasets using clustering and pseudo-labeling techniques. We implement models considering ensemble methods and deep learning algorithms. We perform ten experiments and compare the performance of these code smells-based datasets. We perform binary classification of faults and results are evaluated using multiple evaluation measures. Besides, the results of models are cross-validated using k-fold cross-validation. We use statistical tests to observe the significance of the model. The comparative analysis of experimental results demonstrates that the ensemble method and deep learning approach using code smells and metric dataset is effective for code smell-based defect prediction. The results obtained from our models outperform the state-of-the-art approaches. Datasets including code smells and metric perform better than other smell related datasets. We conclude that code smells-based software defects prediction has optimal accuracy and precision.