You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
    ss
  • technical reports and other articles issued by Mälardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

A counter-example guided abstraction refinement scheme for parameterized verification

Speaker:

Dr. Ahemed Rezine, Uppsala university

Type:

Seminar

Start time:

2010-11-25 11:15

End time:

2010-11-25 12:15

Location:

U2-032

Contact person:



Description

I will introduce ''monotonic abstraction'' as an approach to verify systems with an arbitrary number of concurrent and communicating processes, i.e., parameterized systems. Monotonic abstraction is particularly successful in automatic verification of safety properties for parameterized systems. The main drawback is that it sometimes generates spurious counter-examples.I will describe a counterexample-guided abstraction refinement (CEGAR) framework for monotonic abstraction. The CEGAR algorithm automatically extracts from each spurious counterexample a set of configurations called a “Safety Zone”and uses it to refine the abstract transition system of the next iteration. This approach gave encouraging results and allowed the verification of several parameterized systems.