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Artificial intelligence (AI) refers to a machine's capacity for operations typically performed by human intelligence, such as learning, thinking, solving problems, and making decisions. Machine learning, neural networks, expert systems, and rule-based systems are all used in artificial intelligence. AI employs methods and algorithms to process data, draw conclusions from patterns and laws, and enhance performance over time. A software application or product's intended functionality is evaluated and verified through the process of software testing. The benefits of testing include the prevention of bugs, decreased development costs, and improved performance. Through test generation, test data generation, and automated test script writing, AI can be used in software testing to enhance the quality of our product and the manual testing processes. Software testing is a time-consuming, laborious, and tiresome process. Automation solutions have been created to help with automating some testing process operations in order to increase quality and delivery time. As continuous integration and delivery (CI/CD) pipelines are added, automation systems gradually lose part of their usefulness. The testing community is looking to AI to fill the gap because AI has the capacity to check the code for flaws and defects without the need for any human intervention and much more quickly than humans. In this study, we want to comprehend the effects of AI technology on various STLC tasks or components of software testing. The study also makes an effort to pinpoint and explain some of the biggest challenges faced by software testers when implementing AI in testing. The report also suggests several significant potential contributions of AI to the field of software testing.
- et al. (Fri,) studied this question.