Describe The Process Of Improving Software Fault Prediction Through The Use Of Hybrid Machine Learning Algorithms

Abstract

In contemporary software development, it is imperative to guarantee the dependability and quality of software. The identification of potential defects in software systems prior to deployment is a critical function of Software Fault Prediction (SFP), which in turn reduces maintenance costs, improves reliability, and improves the overall quality of software. Although conventional defect prediction models are somewhat effective, they frequently encounter issues such as data imbalance, poor generalization, and the inability to manage high-dimensional software metrics. This research suggests a hybrid machine learning framework that incorporates feature selection techniques, deep learning, and ensemble learning to improve the prediction of software faults in order to overcome these challenges. The hybrid model that has been proposed combines Deep Neural Networks (DNN) and Gradient Boosting Decision Trees (GBDT) to capitalize on their respective strengths in the learning of complex data patterns and the generation of reliable predictions. In order to enhance the efficacy of the model by reducing dimensionality and eliminating redundant features, feature selection methods such as Recursive Feature Elimination (RFE) and Mutual Information (MI) are implemented. The model is trained and evaluated using publicly available NASA MDP and PROMISE repository datasets, which include core software metrics such as lines of code (LOC), cyclomatic complexity, coupling, and cohesiveness. The hybrid model outperforms conventional machine learning classifiers, such as Support Vector Machines (SVM), Random Forest, and Gradient Boosting, across a variety of performance metrics, as evidenced by experimental results. The hybrid model considerably improves the reliability of software fault detection by achieving a 92% accuracy, 91% generalization score, and a low false positive rate of 7%. Additionally, it maintains a high F1-score (90.5%) and AUC-ROC (93%), which guarantees enhanced precision and recall in the identification of failed software components. The hybrid machine learning framework that has been proposed improves scalability, reduces misclassification errors, and enhances defect prediction accuracy by incorporating adaptive learning, feature selection, and ensemble techniques. The results of this study indicate that hybrid models are a valuable instrument for software quality assurance and defect management in real-world applications, as they can effectively address critical challenges in software fault prediction.