Identifying Research Gaps in AI-Driven Software Testing: A Review of Automation Tools and Challenges in SMEs

Abstract

AI-driven (Artificial Intelligence) software testing frameworks have become crucial in today's fast-paced digital environment to guarantee the timely delivery of high-quality systems. Software testing is undergoing a revolution because of the incorporation of AI-driven frameworks, which automate intricate test scenarios, improve accuracy, and drastically cut down on time-to-market. However, a lack of defined frameworks, cost concerns, and ambiguity over performance hinder the adoption of advanced AI-powered testing solutions by many SMEs (Small and Medium-Sized Enterprises). Researchers and industry professionals will use the review's conclusions as a starting point to close the gap between innovation and real-world application, guaranteeing the smooth incorporation of AI-driven testing frameworks into contemporary software engineering procedures. Initially, 3998 research papers were extracted and at the third filtering, 20 research papers were chosen for the final review. This study offers a comprehensive analysis of the commonly used automation tools in various stages of software testing. The findings of this literature review study suggest there are no experience-based testing approaches for SMEs. There is a need to conduct surveys with practitioners to identify the benefits and limitations they are experiencing from using these automation tools for testing and ultimately to provide a comprehensive framework for automation software testing.

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