On application of data mining in functional debug

Abstract

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.