Smart Test Automation Framework Using AI

Abstract

The research paper aims at establishing a Smart Test Automation Framework which incorporates AI for the development as well as the deployment of a test automation framework. The key domains sought to solve include feature identification, test case generation, test case prioritization, and defect localization – all of which has been seen as problematic in classical test automation methodologies that incorporate AI techniques like machine learning and natural language processing. This paper aims at evaluating the AI algorithm in generative testing, test case and test suite execution, and reporting specifically on the goals of test coverage, test case maintenance, and overall, testing efficiency. The outcomes of the proposed framework are tested and analyzed through several experiments, proving substantial enhancements in the quality of test automation against traditional approaches.