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An EFSM-Based Test Data Generation Approach in Model-Based Testing

Muhammad Luqman Mohd-Shafie1,*, Wan Mohd Nasir Wan Kadir1, Muhammad Khatibsyarbini1, Mohd Adham Isa1, Israr Ghani1, Husni Ruslai2

1 Department of Software Engineering, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia
2 GATES IT Solution Sdn Bhd, WISMA GATES, Jalan Perdana 4, Taman Sri Pulai Perdana 2, 81300, Skudai, Johor, Malaysia

* Corresponding Author: Muhammad Luqman Mohd-Shafie. Email: email

Computers, Materials & Continua 2022, 71(3), 4337-4354.


Testing is an integral part of software development. Current fast-paced system developments have rendered traditional testing techniques obsolete. Therefore, automated testing techniques are needed to adapt to such system developments speed. Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically. It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual. An automatic and effective TDG can further reduce testing cost while detecting more faults. This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model (EFSM). The proposed approach integrates MBT with combinatorial testing. The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach. The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventional MB-TCG but at the same time generated 43 more tests. The proposed approach effectively detects faults, but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.


Cite This Article

M. Luqman Mohd-Shafie, W. Mohd Nasir Wan Kadir, M. Khatibsyarbini, M. Adham Isa, I. Ghani et al., "An efsm-based test data generation approach in model-based testing," Computers, Materials & Continua, vol. 71, no.3, pp. 4337–4354, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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