Intelligent Software Testing System Using Machine Learning

Author: Pratheepraj s, Sharan k and Mrs.R.Surya Prabha

Published On: 2026-03-13

Software testing is an essential phase in the software development life cycle that ensures the reliability and quality of software applications. Traditional testing approaches often rely on manual effort or rule-based automation, which may be time-consuming and inefficient when dealing with large and complex software systems. This research proposes an Intelligent Software Testing System that uses machine learning techniques to improve the efficiency and accuracy of software testing processes. The system analyzes historical test execution data, code changes, and test results to identify patterns that indicate potential software defects and unstable test cases. Machine learning algorithms are used to predict the likelihood of test failures and prioritize test cases based on risk and impact. By automatically identifying problematic areas in the code-base, the proposed system helps developers focus on critical tests and reduce unnecessary testing efforts. Experimental evaluation shows that the intelligent testing system can enhance defect detection capability, reduce testing time, and improve the overall reliability of automated testing pipelines. The study demonstrates that integrating machine learning with software testing can significantly improve software quality and testing efficiency.

Keyword-Machine Learning, Software Testing, Test Automation, Defect Prediction, Intelligent Systems