Enhancing Software Testing Accuracy Through a Generative AI-Powered Framework

Abstract

Software testing is an essential part of the software development lifecycle that ensures the dependability and quality of software systems. However, traditional testing methods typically fail to detect flaws, resulting in potential software breakdowns and increased rectification costs. In response, this research aims to a revolutionary approach to improving software testing accuracy by generating test cases based on a Generative AI-Powered Framework. Using the power of generative artificial intelligence, this framework attempts to intelligently construct diverse and complex test cases, discovering hidden flaws that standard testing approaches may miss. This system employs strong machine learning algorithms to assess code structures and historical testing data, enabling the construction of custom test scenarios based on the program's specific properties. In addition, this incorporates feedback mechanisms for continuously refining and enhancing the testing process, resulting in improved overall testing accuracy and efficiency. This study demonstrates the utility and superiority of the proposed framework in enhancing software testing accuracy, hence raising the bar for software quality assurance operations. This system has the potential to result in better safer and reliable software solutions by boosting test coverage and maybe reducing the number of undetected problems. This study increases AI-powered software testing, paving the way for further usage of Artificial Intelligence technology in the software development process.