Skip Navigation



Logic Journal of IGPL Advance Access published online on August 5, 2006
Logic Journal of IGPL, doi:10.1093/jigpal/jzl007
This Article
Right arrow Full Text (Rapid PDF)
Right arrow All Versions of this Article:
14/5/729    most recent
jzl007v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Groce, A.
Right arrow Articles by Yannakakis, M.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author, 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 19, 2005

Original Papers

Adaptive Model Checking

Alex Groce 1, Doron Peled 2, and Mihalis Yannakakis 3

1 Laboratory for Reliable Software, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
2 Department of Computer Science, Bar-Ilan University, Ramat Gan 52900, Israel
3 Department of Computer Science, Columbia University, 455 Computer Science Building, 1214 Amsterdam Ave., New York, NY 10027, USA


  Abstract

We consider the case where inconsistencies are present between a system and its corresponding model, used for automatic verification. Such inconsistencies can be the result of modeling errors or recent modifications of the system. Despite such discrepancies, we can still attempt to perform automatic verification. In fact, as we show, we can sometimes exploit the verification results to assist in automatically learning the required updates to the model. In a related previous work, we have suggested the idea of black box checking, where verification starts without any model, and the model is obtained while repeated verification attempts are performed. Under the current assumptions, an existing inaccurate (but not completely obsolete) model is used to expedite the updates. We use techniques from black box testing and machine learning. We present an implementation of the proposed methodology called AMC (for Adaptive Model Checking). We discuss some experimental results, comparing various tactics of updating a model while trying to perform model checking.

Keywords: Black box testing; learning regular languages; model checking.
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.