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

TL;DR

This paper reviews and compares recent assertion mining techniques in Assertion-Based Verification, highlighting their capabilities, limitations, and future research directions to improve hardware verification processes.

Contribution

It provides a comprehensive analysis of existing assertion miners, identifying gaps and proposing future directions for more effective assertion mining methods.

Findings

Comparative analysis of assertion mining techniques

Identification of current limitations in assertion miners

Suggestions for future research in assertion mining

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.