Feature selection is essential to rule learning in the context of functional verification. In practice today, features are selected manually and the selection requires domain knowledge. In contrast, this work proposes using automatic feature extraction from design documents as a viable approach to support rule learning. To demonstrate its effectiveness, document-extracted features are employed to learn the rules for covering a set of assertions based on a commercial SoC. Experiments show that 100%-accurate rules can be obtained for more than 70% of the assertions.

Security verification relies on using direct tests manually prepared. Test preparation often requires intensive efforts from experts with in-depth domain knowledge. This work presents an approach to learn from direct tests written by an expert. After the learning, the learned model acts as a surrogate for the expert to produce new tests. The learning software comprises a database for accumulating and sharing security verification knowledge. The learning approach uses process discovery to build an upper-bound model and continuously adds constraints to refine it. We demonstrate the feasibility and effectiveness of the learning approach in a commercial SoC verification environment.

This paper investigates how data mining can be applied in functional debug, which is formulated as the problem of explaining a functional simulation error based on human-understandable machine states. We present a rule discovery methodology comprising two steps. The first step selects relevant state variables for constructing the mining dataset. The second step applies rule learning to extract rules that differentiates the tests that excite error behavior from those that do not. We explain the dependency of the second step on the first step and considerations for implementing the methodology in practice. Application of the proposed methodology is illustrated through experiments conducted on a recent commercial SoC design.