DocumentCode :
2916497
Title :
Finding liveness errors with ACO
Author :
Chicano, Francisco ; Alba, Enrique
Author_Institution :
Dept. de Lenguajes y Cienc. de la Comput., Univ. de Malaga, Malaga
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
2997
Lastpage :
3004
Abstract :
Model checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most of model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of memory if the checked model is large. We propose here the use of an algorithm based on ACOhg, a new kind of ant colony optimization model, to search for liveness property violations in concurrent systems. This algorithm has been previously applied to the search for safety errors with very good results and we apply it here for the first time to liveness errors. The results state that our algorithmic proposal, called ACOhg-live, is able to obtain very short error trails in faulty concurrent systems using a low amount of resources, outperforming by far the results of nested-DFS, the traditional algorithm used for this task in the model checking community and implemented in most of the explicit state model checkers. This fact makes ACOhg-live a very suitable algorithm for finding liveness errors in large faulty concurrent systems, in which traditional techniques fail because of the model size.
Keywords :
optimisation; program verification; ant colony optimization model; concurrent systems; liveness errors; liveness property violations; model checking community; safety errors; software properties; state model checkers; temporal logic formulae; Aerospace engineering; Ant colony optimization; Automata; Automatic logic units; Computer errors; Contracts; Formal verification; Proposals; Read only memory; Safety;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4631202
Filename :
4631202
Link To Document :
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