Software Defect Prediction for Reliability Analysis Using Machine Learning Approach

Ensuring software dependability is of utmost importance in today's technology-driven world, given the increasing significance of software and the potential consequences of defects, such as financial losses and security breaches. The substantial contribution of the software industry to the global economy further emphasizes the need for reliable software. While various methodologies and techniques have been introduced to enhance software reliability, challenges persist, including accurate failure parameter estimation, statistical significance determination, and dealing with imbalanced data. This paper explores machine learning algorithms and defect prediction approaches to address these challenges. The research aims to improve the accuracy of software reliability predictions by utilizing advanced techniques and methodologies. The results demonstrate a substantial improvement in accuracy, with an average of 99.36% across all software systems. The proposed technique showcases its capability to make highly accurate predictions for diverse software projects, offering valuable insights to software development teams and stakeholders. Identifying potential issues and improving software quality become more achievable with the help of the proposed approach.