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Logic Journal of IGPL Advance Access originally published online on August 5, 2006
Logic Journal of IGPL 2006 14(5):729-744; doi:10.1093/jigpal/jzl007
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© The Author, 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Adaptive Model Checking

Alex Groce

Laboratory for Reliable Software, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA.

Doron Peled

Department of Computer Science, Bar-Ilan University, Ramat Gan 52900, Israel.

Mihalis Yannakakis

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.

Key Words: Black box testing • learning regular languages • model checking


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