Combining model checking and testing with an application to reliability prediction and distribution

  • Authors:

  • Lin Gui;Jun Sun;Yang Liu;Yuan Jie Si;Jin Song Dong;Xin Yu Wang

  • Affiliations:

  • National University of Singapore, Singapore;Singapore University of Technology and Design, Singapore;Nanyang Technological University, Singapore;Zhejiang University, China;National University of Singapore, Singapore;Zhejiang University, China

  • Venue:
  • Proceedings of the 2013 International Symposium on Software Testing and Analysis
  • Year:
  • 2013

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Abstract

Testing provides a probabilistic assurance of system correctness. In general, testing relies on the assumptions that the system under test is deterministic so that test cases can be sampled. However, a challenge arises when a system under test behaves non-deterministiclly in a dynamic operating environment because it will be unknown how to sample test cases. In this work, we propose a method combining hypothesis testing and probabilistic model checking so as to provide the ``assurance" and quantify the error bounds. The idea is to apply hypothesis testing to deterministic system components and use probabilistic model checking techniques to lift the results through non-determinism. Furthermore, if a requirement on the level of ``assurance" is given, we apply probabilistic model checking techniques to push down the requirement through non-determinism to individual components so that they can be verified using hypothesis testing. We motivate and demonstrate our method through an application of system reliability prediction and distribution. Our approach has been realized in a toolkit named RaPiD, which has been applied to investigate two real-world systems.