Yoav Katz, Michal Rimon, et al.
DAC 2011
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. © Springer-Verlag 2009.
Yoav Katz, Michal Rimon, et al.
DAC 2011
Biruk Mammo, Debapriya Chatterjee, et al.
DATE 2012
Ronen Kat, Doron Chen, et al.
SYSTOR 2024
Dorit Baras, Shai Fine, et al.
International Journal on Software Tools for Technology Transfer