article

Using Bayesian networks and virtual coverage to hit hard-to-reach events

Authors:

Laurent Fournier,

Avi ZivAuthors Info & Claims

International Journal on Software Tools for Technology Transfer (STTT), Volume 11, Issue 4

Pages 291 - 305

Published: 01 October 2009 Publication History

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.

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Cited By

Pfeifer NZimpel BAndrade GSantos LDi Natale GFummi F(2020)A reinforcement learning approach to directed test generation for shared memory verificationProceedings of the 23rd Conference on Design, Automation and Test in Europe10.5555/3408352.3408474(538-543)Online publication date: 9-Mar-2020
https://dl.acm.org/doi/10.5555/3408352.3408474

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Published In

cover image International Journal on Software Tools for Technology Transfer (STTT)

International Journal on Software Tools for Technology Transfer (STTT) Volume 11, Issue 4

October 2009

84 pages

ISSN:1433-2779

EISSN:1433-2787

Issue’s Table of Contents

Copyright © Copyright © 2009 Springer-Verlag.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2009

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Cited By

Pfeifer NZimpel BAndrade GSantos LDi Natale GFummi F(2020)A reinforcement learning approach to directed test generation for shared memory verificationProceedings of the 23rd Conference on Design, Automation and Test in Europe10.5555/3408352.3408474(538-543)Online publication date: 9-Mar-2020
https://dl.acm.org/doi/10.5555/3408352.3408474

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