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Editor’s note: This article points out that the fundamental problem of platform verification is incompleteness of the test plan and proposes an unsupervised learning approach to augment the test plan.–Magdy Abadir, Helic Inc.
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...
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...
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...
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