Automatic assertion extraction via sequential data mining of simulation traces

This paper studies the problem of automatic assertion extraction at the input boundary of a given unit embedded in a system. This paper proposes a data mining approach that analyzes simulation traces to extract the assertions. We borrow two key concepts from the sequential data mining and develop an effective assertion extraction approach specific to our problem. These two concepts are (1) the slide-window-based episode definition that decides the space of all potential assertions and (2) the Support-Confidence framework that evaluates the meaningfulness of potential assertions using a given simulation trace. We implement the approach in a system simulation environment built on the AMBA 2.0 standard. Experimental results demonstrate the feasibility of the proposed approach and validity of extracted assertions are verified by comparing to the transactions defined in the specification.