Paper Info
Title
Using Bayesian networks and virtual coverage to hit hard-to-reach events
Abstract
Reaching hard-to-reach coverage events is a difficult task that requires both time and expertise. Data-driven coverage directed generation (CDG) can assist in the task when the coverage events are part of a structured coverage model, but is a priori less useful when the target events are singular and not part of a model. We present a data-driven CDG technique based on Bayesian networks that can improve the coverage of cross-product coverage models. To improve the capability of the system, we also present virtual coverage models as a means for enabling data-driven CDG to reach singular events. A virtual coverage model is a structured coverage model (e.g., cross-product coverage) defined around the target event, such that the target event is a point in the structured model. The CDG system can exploit this structure to learn how to reach the target event from covered points in the structured model. A case study using CDG and virtual coverage to reach a hard-to-reach event in a multi-processor system demonstrates the usefulness of the proposed method.
Year
DOI
Venue
2009
10.1007/s10009-009-0119-0
STTT
Keywords
Field
DocType
structured model,virtual coverage,cross-product coverage,bayesian network,coverage event,data-driven coverage,target event,present virtual coverage model,cross-product coverage model,functional verification · functional coverage · coverage directed generation · bayesian networks,hard-to-reach event,hard-to-reach coverage event,structured coverage model,bayesian networks,functional verification
Data mining,Functional verification,Computer science,A priori and a posteriori,Circuit design,Exploit,Theoretical computer science,Bayesian network,Artificial intelligence,Process capability index
Journal
Volume
Issue
ISSN
11
4
1433-2787
Citations 
PageRank 
References 
2
0.51
12
Authors
3
Name
Order
Citations
PageRank
Shai Fine11112107.56
Laurent Fournier211111.09
Avi Ziv346572.49