AI-Driven Software Testing: A Review
TL;DR
An application-focused review of 35 selected empirical studies focusing on the use of AI during software testing, based on PRISMA guidelines, reveals that large language models, machine learning, and computer vision can significantly improve testing efficiency.
Abstract
The rapid evolution of software complexity demands more efficient and autonomous testing mechanisms. Artificial intelligence (AI) has emerged as a solution to the limitations of traditional manual testing in software development, which is time-consuming, prone to human error, and unable to scale with the increasing size and complexity of modern software systems. In this context, this paper presents an application-focused review of 35 selected empirical studies focusing on the use of AI during software testing, based on PRISMA guidelines. We introduce a comprehensive taxonomy categorizing current research into six core fields, including test case generation, defect prediction, and AI model verification. The analysis reveals that large language models, machine learning, and computer vision can significantly improve testing efficiency. Key findings demonstrate that AI can autonomously repair broken test scripts, generate robust synthetic data, enable codeless web testing, and accurately predict system defects before execution. Furthermore, advanced techniques such as reinforcement learning and deep learning successfully validate complex environments, including cloud robotics and quantum software. However, our qualitative and quantitative synthesis also highlights that challenges, such as generative AI “hallucinations” and the brittleness of Continuous Integration and Continuous Deployment (CI/CD) integration, persist. Ultimately, this review proposes a tailored research roadmap for robust industrial adoption, showing that AI is changing the way software is tested, shifting it from a predominantly reactive and static activity toward a proactive, intelligence-driven discipline.