Randomization Helps in LTL Model Checking

Authors:
Lubos Brim;Ivana Cerna;Martin Necesal
Affiliations:
-;-;-
Venue:
PAPM-PROBMIV '01 Proceedings of the Joint International Workshop on Process Algebra and Probabilistic Methods, Performance Modeling and Verification
Year:
2001

Citing 7
Cited 4

Memory-efficient algorithms for the verification of temporal properties

Formal Methods in System Design - Special issue on computer-aided verification: general methods
The complexity of probabilistic verification

Journal of the ACM (JACM)
State-space caching revisited

Formal Methods in System Design
An Analysis of Bitstate Hashing

Formal Methods in System Design
Model Checking of Probabalistic and Nondeterministic Systems

Proceedings of the 15th Conference on Foundations of Software Technology and Theoretical Computer Science
Reliable Hashing without Collosion Detection

CAV '93 Proceedings of the 5th International Conference on Computer Aided Verification
Model checking for a probabilistic branching time logic with fairness

Distributed Computing

A bounded statistical approach for model checking of unbounded until properties

Proceedings of the IEEE/ACM international conference on Automated software engineering
Monte carlo model checking

TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Larger automata and less work for LTL model checking

SPIN'06 Proceedings of the 13th international conference on Model Checking Software
Nested Emptiness Search for Generalized Büchi Automata

Fundamenta Informaticae - APPLICATION OF CONCURRENCY TO SYSTEM DESIGN (ACSD'04)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present and analyze a new probabilistic method for automata based LTL model checking of non-probabilistic systems with intention to reduce memory requirements. The main idea of our approach is to use randomness to decide which of the needed information (visited states) should be stored during a computation and which could be omitted. We propose two strategies of probabilistic storing of states. The algorithm never errs, i.e. it always delivers correct results. On the other hand the computation time can increase. The method has been embedded into the SPIN model checker and a series of experiments has been performed. The results confirm that randomization can help to increase the applicability of model checkers in practice.