The Uneven Journey of AI in Software Testing: A Maturity Model for Industry Adoption

Abstract

Software testing plays a significant role in the development cycle process because it is necessary to ensure high-quality software and meet the client’s requirements. However, it is considered one of the most time and cost consuming activities in the development process. On the other hand, the integration of AI in software testing promises vital advances in terms of efficiency, coverage, and time saving, but the evolving software testing tools sector remains relatively conservative due to several challenges related to trustworthiness, scalability, customization, and ethical considerations. In this paper, we propose a maturity model for industry adoption of AI in software testing, designed to help organizations assess and advance their integration efforts. Grounded in a multi-stakeholder ecosystem perspective incorporating academia, PhD researchers, industry players, and governmental agencies our model identifies key stages of adoption and the conditions necessary for progress. By reframing the gap between innovation and practice through this structured lens, we offer actionable insights to align research outputs with industrial readiness and accelerate effective AI adoption in testing environments.