Application of AI to formal methods - an analysis of current trends

2024-11-22

arXiv.org

AI Summary

This study addresses the lack of systematic synthesis in research on the integration of artificial intelligence (AI) and formal methods (FM). It presents the first systematic mapping study (SMS) of the AI×FM interdisciplinary domain covering 2019–2023, synthesizing 189 empirical studies. Employing a mixed-methods approach—combining bibliometric analysis, thematic clustering, and temporal trend analysis—it transcends the conventional dichotomy between “black-box AI” and “verifiable FM.” The study identifies six distinct AI-enabled FM application patterns, three cross-cutting technical challenges, and four prioritized research directions. These findings provide an evidence-based foundation and strategic roadmap for the co-evolution of trustworthy AI and formally verifiable software, thereby filling a critical gap in systematic reviews of this emerging interdisciplinary field.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

With artificial intelligence (AI) being well established within the daily lives of research communities, we turn our gaze toward an application area that appears intuitively unsuited for probabilistic decision-making: the area of formal methods (FM). FM aim to provide sound and understandable reasoning about problems in computer science, which seemingly collides with the black-box nature that inhibits many AI approaches. However, many researchers have crossed this gap and applied AI techniques to enhance FM approaches. As this dichotomy of FM and AI sparked our interest, we conducted a systematic mapping study to map the current landscape of research publications. In this study, we investigate the previous five years of applied AI to FM (2019-2023), as these correspond to periods of high activity. This investigation results in 189 entries, which we explore in more detail to find current trends, highlight research gaps, and give suggestions for future research.

Problem

• Analyzing AI applications in formal methods trends
• Identifying how formal methods benefit from AI
• Highlighting research gaps and future directions

Innovation

• AI applied to formal methods
• Systematic mapping study methodology
• Identified gaps in theoretical groundwork