Cross-product functional coverage analysis using machine learning clustering techniques

Abstract

This work proposes the application of clustering machine learning to simplify functional coverage analysis. It introduces a two-round clustering algorithm to group the functional coverage goals that share similar cover items. In the first round, the associations between cover-crosses are encoded as a binary connectivity matrix. K-Means with Jaccard similarity is used to group highly correlated cover-crosses. In the second round, coverage ratio is used as the main measure to sub-group the clusters resulted from the first round. The resulted clusters are then analyzed to identify which cover-crosses mostly contribute to low coverage clusters. Dropping the number of cover-crosses to analyze into a limited number of representative buckets that can further be used by advanced analysis engines to help reach coverage closure faster.