Estimation of Software Reliability Testing Using Machine Learning Techniques

This research article addresses the growing need for accurate forecasts in software quality assurance by means of advanced machine learning approaches, “therefore estimating the degree of program reliability. Conventional models of dependability sometimes neglect the complexity and changing nature of modern software systems, therefore affecting the risk assessment and resource allocation. By analyzing fault distributions, use patterns, and defect trends, we use machine learning techniques including decision trees, support vector machines, and neural networks to replicate software dependability in this work. The approach consists on gathering failure data from actual software projects, then in feature engineering and predictive model development. To see just how well such models are adaptable on a range of computing systems, we examined the precision, accuracy, and scalability of these models. Due to this fact, the machine learning approaches turn out to be very strong in comparison with traditional methods of reliability estimation since they enhanced reliability predictions can be given with these methods, and also more detailed information can be obtained regarding the locations of potential failures. Better risk management, more effective maintenance plans, and optimum testing methodologies are just some of the practical implications of these discoveries that this paper covers. Software failures can cause huge impacts across sectors in today's era; hence, this work will contribute to the creation of more resilient and fault-tolerant systems by embedding machine learning into software dependability estimation. It changes the paradigm in software engineering.