AN EVOLUTIONARY APPROACH FOR ROBUSTNESS TESTING

Thaise Yano, Eliane Martins, Fabiano L. de Sousa

Abstract

In this paper we present an evolutionary testing approach to automatically generate robustness test sequences to test a communication protocol, modeled as an extended finite state machine (EFSM). The model represents the normal situation as well as in presence of faults, which makes the model too large to be treated by conventional test case generation approaches, because of the risk of combinatorial explosion. To cope with this problem, we use a testing approach based on test purposes. To search sequences that satisfy the test purposes, we use two evolutionary algorithms: the Generalized Extremal Optimization (GEO) and a Genetic Algorithm (GA). For the moment, only the control flow part of the model is taken into account. Results show that the approach is viable and potentially useful to consider data flow part of complex EFSM models.

References

  1. Abreu, B. T., Martins, E., and Sousa, F. L. (2007). Generalized extremal optimization: a competitive algorithm for test data generation. In 21st Brazilian Symposium on Software Engineering, Joa˜o Pessoa, Brazil.
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  3. Briand, L. C., Labiche, Y., and Shousha, M. (2005). Stress testing real-time systems with genetic algorithms. In
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Paper Citation


in Harvard Style

Yano T., Martins E. and de Sousa F. (2009). AN EVOLUTIONARY APPROACH FOR ROBUSTNESS TESTING . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 277-280. DOI: 10.5220/0002294102770280


in Bibtex Style

@conference{icec09,
author={Thaise Yano and Eliane Martins and Fabiano L. de Sousa},
title={AN EVOLUTIONARY APPROACH FOR ROBUSTNESS TESTING},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)},
year={2009},
pages={277-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002294102770280},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)
TI - AN EVOLUTIONARY APPROACH FOR ROBUSTNESS TESTING
SN - 978-989-674-014-6
AU - Yano T.
AU - Martins E.
AU - de Sousa F.
PY - 2009
SP - 277
EP - 280
DO - 10.5220/0002294102770280