Journal of Product Design, Quality Engineering & Technology ISSN: 3139-2008 (Online)

Abstract

Testing and verification are critical activities across software engineering, hardware design, cyber-physical systems, and product development lifecycles. Traditional testing approaches rely heavily on manually designed test cases, rule-based automation, and exhaustive verification strategies that are often time-consuming, expensive, and unable to cope with growing system complexity. In recent years, Artificial Intelligence (AI) has emerged as a transformative enabler for testing and verification automation. AI-enhanced methods leverage machine learning, deep learning, natural language processing, and search-based optimization to improve test generation, fault detection, coverage analysis, and verification efficiency. This paper presents a comprehensive review of AI-enhanced testing and verification automation, discussing its evolution, core techniques, applications across industries, benefits, challenges, and future research directions. The review highlights how AI-driven testing improves scalability, reduces human effort, and enhances reliability, while also acknowledging limitations such as data dependency, explainability issues, and trust concerns. The paper aims to serve as a reference for researchers and practitioners working in product design, quality engineering, and advanced technology domains.

Keywords: AI-driven testing, verification automation, machine learning, software quality, intelligent test generation, digital systems