Abstract Software quality is undergoing a profound transformation, driven by state-of-the-art research on the application of emerging technologies in software development processes. Specifically, the use of generative Artificial Intelligence (AI) may represent an opportunity to advance the state of practice in this domain. This study aims to assess the industrial readiness and availability of Generative AI-based solutions for software quality, classifying them according to ISO/IEC 25010 attributes and SDLC phases. An empirical assessment of the state of practice was conducted, employing a Rapid Multivocal Literature Review (RMLR) protocol as a data collection instrument to screen evidence from academic databases (Scopus) and grey literature (Google, GitHub, PapersWithCode). We identified 24 potentially usable solutions. However, the analysis reveals a low technological maturity, with most solutions being academic prototypes hampered by fundamental technical limitations and adoption challenges. These include the “last mile problem” in translating research prototypes into reliable, production-ready tools; the “strategic adoption dilemma” forcing practitioners to trade off between proprietary lock-in and high open-source infrastructure costs; and the “scarcity of realistic public data,” which drives a generalization gap due to reliance on synthetic or leaked benchmarks. Generative AI in software quality remains an emerging but immature field, hampered by a critical reliability gap between academic prototypes and industrial needs. Advancing this domain requires moving beyond a narrow code-centric focus to address the quality of the AI systems themselves, expanding research across all SDLC phases and ISO 25010 attributes. We conclude with a roadmap advocating for contamination-free benchmarks, explainable architectures, and robust guidelines for real-world integration.
Gheventer et al. (Wed,) studied this question.