Object-Oriented Metrics and Machine Learning–based Early Prediction of Software Reliability: An Analytical Framework
Abstract
Achieving high reliability is essential for ensuring that software systems operate successfully in real-world environments, particularly in high-assurance domains. As modern software systems become larger and more complex, predicting reliability at an early stage of development has become essential for reducing maintenance costs, improving testing strategies, and ensuring dependable software delivery. This paper explores the early prediction of software reliability by leveraging object-oriented metrics with machine learning techniques. It presents methodologies for assessing software reliability at early stages of the software development lifecycle, using metrics such as coupling, cohesion, inheritance, and complexity. The framework utilizes historical software datasets containing design metrics, defect information, and reliability indicators to develop predictive models. Machine learning algorithms such as Random Forest, Support Vector Machine, and Artificial Neural Networks are applied to identify complex relationships between software characteristics and potential failures. The proposed framework supports proactive software quality management by helping developers prioritize testing, optimize resource allocation, and improve design decisions before deployment. This approach contributes toward developing reliable, cost-effective, and high-quality software systems while reducing long-term maintenance efforts.
The paper reviews key machine learning algorithms that have been employed to model software reliability prediction and analyzes case studies that demonstrate the effectiveness of these approaches. The findings suggest that certain OO metrics can serve as significant indicators of software reliability, facilitating proactive management of software quality.
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