Why does it fail? Explanation of verification failures

2026-03-23

AI Summary

This work addresses the challenge that counterexamples generated by formal verification often consist of numerous low-level Boolean variables, rendering them difficult for developers to interpret at the application-domain level. To bridge this gap, the paper proposes a novel hierarchical explanation method that integrates predicate relevance metrics with dependency graph analysis—a first-time fusion of these two techniques—to automatically extract human-readable, domain-oriented explanations from logical formulas. By leveraging formal modeling and a dedicated explanation-generation algorithm, the approach produces concise and semantically clear descriptions of failure causes across multiple case studies. Empirical results demonstrate that the method significantly outperforms existing techniques, offering effective support for fault localization in practical verification tasks.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Satisfiability solving is a common technique for formal verification forming the basis of many proof and model checking systems. Failure to show a proof obligation will produce a counterexample or failure trace with typically many thousands or even millions of boolean variables. Interpreting such a counterexample poses a challenge. Even if the individual variables are all understood, it is difficult to form a "big picture" of the situation causing the failure. We consider the case where verification conditions are expressed using concepts from a formal application domain model in a language based on predicate logic or a similar language. We introduce a method to explain verification failures in application domain terms. A measure of the relative relevance of predicates is used to extract the parts of a formula most likely to contribute meaningfully to the explanation. Dependencies between predicates are used to form a branching sequence of successive explanations. These explanations can help a practitioner find faults in the system being verified. The method is demonstrated on examples and compared to other methods.

Problem

• formal verification
• verification failure
• counterexample interpretation
• application domain model
• predicate logic

Innovation

• verification failure explanation
• predicate relevance
• dependency-based reasoning
• formal verification
• counterexample interpretation