Evolutionary functional testing of continuous control systems

ABSTRACT

Evolutionary functional testing is an approach to automatically generating test cases that violate a certain functional system requirement. This paper proposes an approach suitable for efficiently testing complex continuous control systems using an evolutionary testing framework that has been optimized for deployment in the industrial domain. Features of this test environment include the ability of automatically generating and optimizing continuous test data sets. The efficiency of the method is assessed by performing an experimental case study and by comparison with random testing. Results of this work indicate high usability and efficiency of the proposed method.

REFERENCES

• 1: André Baresel and Hartmut Pohlheim and Sadegh Sadeghipour, Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms, Proceedings of Genetic and Evolutionary Computation Conference, 2003, 2428--2441
• 2: Oliver Bühler and Joachim Wegener, Evolutionary Functional Testing of a Vehicle Brake Assistant System, Proceedings of 6th Metaheuristics International Conference, 2005
• 3: Maarten Keijzer , Juan J. Merelo Guervós , Gustavo Romero , Marc Schoenauer, Evolving Objects: A General Purpose Evolutionary Computation Library, Selected Papers from the 5th European Conference on Artificial Evolution, p.231-244, October 29-31, 2001
• 4: Harmen Sthamer and Joachim Wegener, Using Evolutionary Testing to improve Efficiency and Quality in Software Testing, Proceedings of 2nd Asia-Pacific Conference on Software Testing, 2002
• 5: Tanja Vos, EvoTest, 2006,http://www.evotest.eu
• 6: Andreas Windisch , Noura Al Moubayed, Signal Generation for Search-Based Testing of Continuous Systems, Proceedings of the IEEE International Conference on Software Testing, Verification, and Validation Workshops, p.121-130, April 01-04, 2009 [doi>10.1109/ICSTW.2009.16]

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.5 Testing and Debugging
          Subjects: Testing tools (e.g., data generators, coverage testing)

General Terms:
Experimentation, Verification

Keywords:
automobile industry, genetic algorithms, optimization, search