Automatic Assertion Mining in Assertion-Based Verification: Techniques, Challenges, and Future Directions

2026-01-05

arXiv.org

AI Summary

This study addresses the inefficiency and insufficient coverage of automatically generated assertions in hardware functional verification by presenting the first systematic survey and comprehensive comparison of prevailing automatic assertion mining techniques. It encompasses a broad spectrum of approaches, including formal methods, machine learning, and program analysis. Through rigorous literature review, methodological comparison, and applicability assessment, the work identifies key bottlenecks and limitations of existing techniques and clarifies the relative strengths and weaknesses of each approach. The findings provide both academia and industry with a principled basis for selecting assertion generation tools and inform future research directions, thereby advancing assertion-based verification (ABV) toward greater efficiency and intelligence.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Functional verification increasingly relies on Assertion-Based Verification (ABV), which has become a key approach for verifying hardware designs due to its efficiency and effectiveness. Central to ABV are automatic assertion miners, which apply different techniques to generate assertions automatically. This paper reviews the most recent, advanced, and widely adopted assertion miners, offering a comparative analysis of their methodologies. The goal is to provide researchers and verification practitioners with insights into the capabilities and limitations of existing miners. By identifying their shortcomings, this work also points toward directions for developing more powerful and advanced assertion miners in the future.

Problem

• Assertion-Based Verification
• Automatic Assertion Mining
• Functional Verification
• Hardware Verification
• Assertion Generation

Innovation

• Assertion-Based Verification
• Automatic Assertion Mining
• Hardware Verification
• Formal Verification
• Assertion Generation