Enhancing Software Reliability Through Machine Learning: Prediction Through Evaluation Metrics

Predicting and managing software faults is crucial in modern development, demanding costeffective and dependable strategies. Traditional approaches based on historical data or modulespecific insights frequently fall short of predictive capabilities. To address this, an automated machine learning-based defect prediction model is proposed, enabling proactive error detection and resolution, thus enhancing software performance and efficiency. This ensures seamless task continuity and bolsters software resilience. Comprehensive evaluation metrics, including accuracy, precision, recall, specificity, F1 measure, and tenfold cross-validation, ensure thorough performance assessment. Integration of DevSecOps principles becomes imperative for bolstering code security and vulnerability mitigation. By leveraging machine learning for defect prediction, the DevSecOps pipeline is strengthened, facilitating early threat identification and mitigation. Prioritizing testing efforts based on predicted fault likelihood enhances resource allocation, optimizing efficiency and software quality. Moreover, a novel methodology automatically extracts semantic representations from source code, bridging the semantic gap inherent in traditional defect prediction systems. Experimental validation using diverse datasets highlights the efficacy of the proposed technique, surpassing established approaches like PCA, LDA, and Kernel PCA(KPCA) in capturing intricate program semantics. Using several classifiers like decision tree, Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN) , and SVM, achieved the accuracy in the article, up to 73 % and by merging all datasets and improving deep learning models i.e. ANN we achieved the papers highest accuracy of up to 95 %. In conclusion, this study presents a comprehensive methodology combining machine learning and deep learning based DevSecOps principles to enhance software defect prediction and testing. By leveraging advanced techniques, software professionals can anticipate and address faults, fortify security, and optimize resource utilization, thereby improving software reliability and efficiency in contemporary development scenarios.