Machine Learning-Based Reliability Evaluation for Software Defect Prediction and Model Validation Assessment

The reliability of software plays a key and decisive role in assessing the quality of software. It is one of the most critical factors to consider before delivering a software product. An integrated data-driven reliability innovative methodology is presented in this paper, which incorporates a machine learning model for defect prediction coupled with its economic feasibility. The combination of ML, real-time ODC data integration, and BOCR analysis for both technical and economic assessment distinguishes this approach from conventional software reliability evaluation methods. The first component of the proposal relies on the application of artificial intelligence, and illustrates in what way machines learn to access big data and train the network along with performance metrics. The second component, validation of the economic feasibility of the machine learning model, was performed by weighing the pros and cons of the envisioned application problem. As a result, the proposed approach supports numerous advantages and potential applications of machine learning models in various interdisciplinary fields to evaluate reliability and further augment industrial globalization. Additionally, the model echoes robustness in executing complex and distributed transactional application problems by addressing a variety of user needs.