Transforming Software Quality Assurance through Artificial Intelligence, Automated Testing, and Intelligent Software Analytics
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I5P109Keywords:
Software Quality Assurance, Artificial Intelligence, Automated Testing, Machine Learning, Intelligent Software Analytics, Defect Prediction, Continuous Integration, Software Reliability, DevOps, Quality EngineeringAbstract
Software Quality Assurance (SQA) plays a vital role in ensuring the reliability and performance of modern software systems. However, the increasing complexity of software applications, rapid development cycles, and adoption of technologies such as CI/CD, cloud computing, microservices, and distributed systems have created new challenges for traditional quality assurance approaches. Manual testing and conventional automation techniques are often unable to meet the demands of these dynamic environments. As a result, Artificial Intelligence (AI), Automated Testing, and Intelligent Software Analytics have emerged as effective solutions for improving software quality and testing efficiency. This study explores the integration of AI-driven techniques into software quality assurance processes and examines their impact on testing effectiveness, defect detection, and overall software reliability. The proposed framework combines machine learning-based defect prediction, automated test execution, and intelligent analytics to support continuous quality monitoring and informed decision-making. By analyzing software development data such as source code, defect records, user feedback, and runtime information, the framework helps identify potential risks and optimize testing activities. The findings indicate that AI-powered quality assurance can significantly improve defect detection rates, increase test coverage, reduce testing time, and lower maintenance costs. Furthermore, intelligent analytics enables organizations to identify quality issues at an early stage and take preventive actions before software release. The study demonstrates that integrating AI, automated testing, and software analytics can create a more efficient and reliable quality assurance ecosystem, supporting faster software delivery while maintaining high quality standards. Future research may focus on explainable AI, autonomous quality engineering systems, and deeper integration with DevSecOps practices.
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