Artificial Intelligence for Verification and Validation of Safety-Critical Systems: A Bibliometric Science-Mapping Study and an Assurance-Level Framework (AVAF) (2016–2026)

Abstract

Artificial intelligence (AI) and formal verification and validation (V&V) are two of the most consequential areas in safety-critical systems engineering, and they interact in a dual way: AI is increasingly used to support V&V, while V&V is increasingly applied to certify AI components whose high-dimensional and emergent behaviors resist classical methods. This study provides one of the first comprehensive bibliometric and science-mapping analyses of AI-assisted V&V for safety-critical systems over 2016–2026, combining, for the first time, Scopus and the Web of Science Core Collection into a single PRISMA 2020-compliant corpus of 425 documents. Descriptive bibliometrics, keyword co-occurrence and thematic mapping, trend-topic detection, and logistic lifecycle modelling reveal a field growing at 21.8% annually, mobilizing 1249 authors across 54 countries and organized into four thematic communities, formal verification with machine learning, model checking with agentic AI and large language models, and AI-driven runtime monitoring of cyber-physical systems, with large language models, theorem proving, and AI-systems verification as the youngest themes. Findings are situated against emerging AI-governance regimes, including the European Union Artificial Intelligence Act, the United States NIST AI Risk Management Framework, and ISO/IEC 42001. Beyond mapping, the study contributes the AI-Verification Assurance Framework (AVAF), a three-layer instrument that codes each study by verification direction, method, and property, grades the assurance it demonstrates on an A0–A4 scale, and maps it to certification regimes; AVAF is calibrated and pilot-tested on a stratified, subsample (intra-coder test–retest Cohen’s κ = 0.84). Its application reveals a certifiability gap: assurance concentrates in bounded-formal and statistical tiers, and no study in the coded calibration subsample attains a complete formal guarantee over a realistic model or certifiable, tool-qualified evidence (A4). The paper closes with a six-point research agenda on the certification, scalability, and multi-domain transfer of AI-assisted verification.