Automatic Test Transition Paths Generation Approach from EFSM Using State Tree

Chen, Yuan; Wang, Anbang; Wang, Junjie; Liu, Luo; Song, Yuanzhang; Ha, Qinghua

doi:10.1109/qrs-c.2018.00029

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The first step in MBT is to build one or more test models. EFSM is one of the common models used in MBT [13,14]. Test models are usually created from informal requirements or specification documents.…”

Section: Model-based Testingmentioning

confidence: 99%

“…A state could be interpreted as the current values of a set of variables that the SUT has [17]. These variables that dictate which state the SUT is currently in are usually called context variables or internal variables [13,14], in contrast to user variables, which hold the user inputs. When a transition is executed, the context variables of the SUT could change, as instructed by the actions, which then lead to a state change.…”

Section: Extended Finite State Machinementioning

confidence: 99%

See 1 more Smart Citation

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

Mohd-Shafie¹,

Kadir²,

Khatibsyarbini³

et al. 2022

Computers, Materials &Amp; Continua

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Testing is an integral part of software development. Current fastpaced 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.

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Section: Model-based Testingmentioning

confidence: 99%