Smart Test Automation: Integrating AI and ML for Continuous Digital Transformation

Abstract

The high rate of digital transformation and the rising complexity of software developed a necessity to explore how Smart Test Automation combined with AI and ML would enhance continuous delivery pipelines. This paper explores the transition of manual testing to intelligent automation to gain insight into the efficiency and reliability of tests through AI-driven capabilities. The main aspects, including autonomous test generation, self-healing scripts, intelligent test prioritization, and defect prediction, contribute to stability, accuracy, and maintainability in testing ecosystems to a great degree. Other important issues in digital transformation identified in the research are cultural resistance, lack of digital skills, infrastructural constraints, and data privacy issues. The analysis of the recent literature shows how the innovations in the field of AI-based automation, Visual AI, script generation with the help of NLP, and simulation tools redefine contemporary testing processes in different industries. In general, the research highlights the fact that AI-enhanced smart automation is the key to facilitating continuous integration, continuous testing, and continuous deployment via human intervention minimization, faster feedback cycles, and scalable and reliable software delivery.