Facebook pixel tracking

The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

Contact Info
Website thesai.org
Follow Us
Contact Info
Follow Us
Research Article | Open Access |

Event-B Control Flow Modeling based on iUML-B State Machine

Author 1: Han Peng Author 2: Chenglie Du Author 3: Haobin Wang
International Journal of Advanced Computer Science and Applications (IJACSA) · Published 2018

DOI: 10.14569/SpecialIssue.2018.090153

Abstract

There are some limitations in expressing the order of actions using Event-B. To solve this problem, the event refinement structure method (ERS) is proposed to facilitate modeling of the system’s control flow. However, the event refinement structure cannot be translated to a behavior semantic model such as the communication sequence process (CSP) or labeled transition system (LTS) directly, thus it is not convenient for engineers to verify the control flow. In this paper, we first propose a general method to model the control flow of the Event-B model with various iUML-B state machines. Then we prove by simulation that the event trace of the iUML-B state machine is the same as that of the event refinement structure method. Finally, we use a case study of a lift control system to prove the practicality of our method.

Keywords

How to Cite this Article

Peng, H., Du, C., & Wang, H. (2018). Event-B Control Flow Modeling based on iUML-B State Machine. International Journal of Advanced Computer Science and Applications, 9(1). https://doi.org/10.14569/SpecialIssue.2018.090153

Peng, Han, et al.. "Event-B Control Flow Modeling based on iUML-B State Machine." International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, 2018, https://doi.org/10.14569/SpecialIssue.2018.090153.

@article{Peng2018,
  title     = {Event-B Control Flow Modeling based on iUML-B State Machine},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {1},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Han Peng and Chenglie Du and Haobin Wang},
  doi       = {10.14569/SpecialIssue.2018.090153},
  url       = {https://doi.org/10.14569/SpecialIssue.2018.090153}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.