ARTmine: Automatic Association Rule Mining with Temporal Behavior for Hardware Verification

Association rule mining is a promising data mining approach that aims to extract correlations and frequent patterns between items in a dataset. On the other hand, in the realm of assertion-based verification, automatic assertion mining has emerged as a prominent technique. Generally, to automatically mine the assertions to be used in the verification process, we need to find the frequent patterns and correlations between variables in the simulation trace of hardware designs. Existing association rule mining methods cannot capture temporal behaviors such as next[N], until, and eventually that hold significance within the context of assertion-based verification. In this paper, a novel association rule mining algorithm specifically designed for assertion mining is introduced to overcome this limit. This algorithm powers ARTmine, an assertion miner that leverages association rule mining and temporal behavior concepts. ARTmine outperforms other approaches by generating fewer assertions, achieving broader design behavior coverage in less time, and reducing verification costs.