In today's world, software testing and quality assurance have become an important part of the software development process. Artificial intelligence technologies have also been introduced in this process. AI-supported software testing and quality assurance is a method used to detect and correct errors in the software development process.\n\nAI technologies are used to analyze and report data used in the software testing and quality assurance process to detect errors. These technologies automatically analyze and report errors in the data used in the software testing and quality assurance process. This makes the detection and correction of errors in the software development process faster and more effective.\n\nAI-supported software testing and quality assurance includes many different technologies used to detect and correct errors in the software development process. These technologies include machine learning, natural language processing, data mining, and artificial neural networks.\n\nMachine learning is one of the most commonly used technologies in AI-supported software testing and quality assurance. This technology is used to analyze and report data used in the software testing and quality assurance process to detect errors. Machine learning automatically analyzes and reports errors in the data used in the software testing and quality assurance process.\n\nNatural language processing is another technology used in AI-supported software testing and quality assurance. This technology is used to analyze and report data used in the software testing and quality assurance process to detect errors. Natural language processing automatically analyzes and reports errors in the data used in the software testing and quality assurance process.\n\nData mining is another technology used in AI-supported software testing and quality assurance. This technology is used to analyze and report data used in the software testing and quality assurance process to detect errors. Data mining automatically analyzes and reports errors in the data used in the software testing and quality assurance process.\n\nArtificial neural networks are another technology used in AI-supported software testing and quality assurance. This technology is used to analyze and report data used in the software testing and quality assurance process to detect errors. Artificial neural networks automatically analyze and report errors in the data used in the software testing and quality assurance process.\n\nAI-supported software testing and quality assurance is an effective method used to detect and correct errors in the software development process. This method includes many different technologies used to detect and correct errors in the software development process, such as machine learning, natural language processing, data mining, and artificial neural networks.