Feature extraction from design documents to enable rule learning for improving assertion coverage
Kuo-Kai Hsieh, Sebastian Siatkowski, Li-C. Wang, Wen Chen, Jayanta Bhadra · 2017
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.