AI-DRIVEN TEST AUTOMATION FRAMEWORKS: ENHANCING EFFICIENCY AND ACCURACY IN SOFTWARE QUALITY ASSURANCE

Abstract

The quick change in software development techniques has increased the challenge of need of effective, dependable, and dynamic testing mechanisms. Manual testing methods and techniques are usually not sufficient to cope with complexities and dynamics of the contemporary software. The paper gives an overview of AI-based test automation systems that build on machine learning (ML), natural language processing (NLP), and deep learning (DL) to increase the efficiency and effectiveness of software quality assurance (SQA). Combining smart algorithms, these frameworks may automatically create, run and streamline test cases, forecast possible defects, and evolve in response to adverse changes in system behavior. Reinforcement learning techniques support steady enhancement of the test coverage, whereas NLP allows one to automatically transform human-readable requirements into executable test scripts. Moreover, AI analytics are also used to predict and prioritize defects, minimizing the test cycle time and human labour. The paper also evaluates the issue of data dependency, model interpretability and integration in continuous integration/continuous deployment (CI/CD) pipelines. In case studies and evaluations through experimentation, there are great increases in success rates in detecting defects and testing efficiency. Finally, AI-based automation systems are a paradigmic shift in the field of SQA, which encourages smarter, scalable and adaptive testing infrastructures, which guarantee increased software dependability and faster release times.