Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms

Abstract

In today’s fast-paced world of software development, it is essential to ensure that programs run smoothly without any issues. When dealing with complex applications, the objective is to predict and resolve problems before they escalate. The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. In semi-supervised learning, we tested are autoencoders, semi-supervised support vector machines, and generative adversarial networks. For self-supervised learning, we utilized are autoencoder, simple framework for contrastive learning of representations, and bootstrap your own latent. After comparing the performance of each machine learning algorithm, we identified the most effective one. Among these, the gradient boosting AdaBoost classifier demonstrated superior performance based on an accuracy of 90%, closely followed by the AdaBoost classifier at 89%. Finally, we applied ensemble methods to predict software defects, leveraging the collective strengths of these diverse approaches. This enables software developers to significantly enhance defect prediction accuracy, thereby improving overall system robustness and reliability.

Keywords

• software defect
• semi-supervised learning
• self-supervised learning
• supervised learning
• ensemble
• machine learning