Particle Swarm Optimization Based Parallel Input Time Test Suite Construction
Testing of Real-Time Embedded Systems (RTESs) under input timing constraints is a critical issue, especially for parallel input factors. Test suites which can cover more possibilities of input time and discover more defects under input timing constraints are worthy of study. In this paper, Parallel Input Time Test Suites (PITTSs) are proposed to improve the coverage of input time combinations. PITTSs not only cover all the neighbor input time point combinations of each factor, but also cover all the input time point combinations between each two factors of the same input. Particle swarm optimization based the PITTS construction algorithm is presented and benchmarks with different configurations are conducted to evaluate the algorithm’ performance. A real world RTES is tested with a PITTS as application and we have reason to believe that PITTSs are effective and efficient for testing RTESs under input timing constraints of parallel input factors.
KeywordsReal-time embedded systems Parallel input Time test suite construction Particle swarm optimization
The research work presented in this paper is supported by National Defense Basic Scientific Research Project of China (xx2016xxxxx). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
- 4.En-Nouaary, A., Hamou-Lhadj A.: A boundary checking technique for testing real-time systems modeled as timed input output automata. In: Eighth International Conference on Quality Software, pp. 209–215. IEEE Computer Society, Washington DC (2008)Google Scholar
- 7.Wei, C.A., Sheng, Y.L., Jiang, S.D.: Combinatorial test suites generation method based on fuzzy genetic algorithm. J. Inf. Hiding Multimed. Signal Process. 6, 968–976 (2015)Google Scholar
- 8.Lei Y., Tai K.C.: In-parameter-order: a test generation strategy for pairwise testing. In: Proceedings of 3rd IEEE International Symposium on High-Assurance Systems Engineering, pp. 254–261. IEEE Computer Society, Washington DC (1998)Google Scholar
- 9.Kennedy, J., Eberhart, R.: Particle swarm optimization. In: ICNN’95—International Conference on Neural Networks, pp. 1942–1948. IEEE, Washington DC (1995)Google Scholar