Abstract The growing complexity of modern software systems has reduced the effectiveness and scalability of traditional software testing methods. Artificial Intelligence (AI) has emerged as a promising approach for enhancing software testing by improving efficiency, accuracy, and test coverage. This paper investigates the role of AI in software testing and presents a comparative analysis between AI-based testing approaches and conventional testing techniques. AI-driven methods, including machine learning, deep learning, and predictive analytics, enable automated test case generation, intelligent defect detection, and adaptive test execution. These capabilities reduce manual effort and support continuous testing in Agile and DevOps environments. However, the adoption of AI in software testing introduces challenges related to data quality, algorithmic bias, ethical concerns, and limited transparency in AI decision-making. This study reviews existing research to analyse the benefits and limitations of AI-based testing, and identifies key research gaps, and highlights future research directions. These findings emphasise the importance of combining human expertise with AI-driven automation to achieve efficient and reliable software testing.
Rutuja Kadam (Sat,) studied this question.