Artificial Intelligence in Software System Quality Assurance: A Systematic Literature Review of Techniques, Tools, and Challenges (2016–2026)
TL;DR
An enormous course in scholarly output from 2023 onwards is revealed by the findings, and this growth is driven by the industrial adoption of Large Language Models alongside autonomous agentic systems.
Abstract
A shift from deterministic testing to artificial intelligence (AI) driven quality ecosystems is necessitated by the rapid evolution of software architectures, and research from 2016 to the present year is combined by this systematic literature review (SLR), so 195 main studies are analyzed, and the path of artificial intelligence in Software Quality Assurance (SQA) is mapped. An enormous course in scholarly output from 2023 onwards is revealed by the findings, and this growth is driven by the industrial adoption of Large Language Models (LLMs) alongside autonomous agentic systems. In addition, three main areas are addressed by this study, and these areas are identified as the taxonomic shift toward multi-agent architectures, the functional effect of AI on labor-intensive activities such as regression testing, and self-healing automation, and the emerging social and technical challenges of ethical governance alongside explainability. Despite incomparable efficiency gains being offered by AI-driven techniques, the industrial success of these tools is strictly limited until the Maintenance Crisis of generated code is resolved and transparency is ensured through Explainable AI (XAI). Finally, the study concludes with a strategic roadmap for Ethical SQA, providing a foundation for future research in autonomous, self-evolving software systems.