Abstract
Automated software testing reduces manual work, increases test coverage, and improves error detection. Model-Based Testing (MBT) is a testing approach that automatically executes test cases generated from a model representing the system behavior. The parallelization of MBT process stages, such as model creation and exploration, or test case generation and execution, could improve its scalability to handle complex systems. Agent-Oriented Software Testing (AOST) refers to the use of intelligent agents focusing on the automation of complex testing tasks. AOST could improve the testing process by providing a high level of decomposition, independence, parallel activation, intelligence, autonomy, sociality, mobility, and adaptation. In this work, we conducted a systematic mapping study of the existing AOST approaches for MBT. We identified 36 primary studies over the period 2002–2020. We classified agent approaches according to the MBT process stages, and tasks and roles covered as part of their implementation. We found 25 implementations of AOST approaches in the test case generation stage, 20 in the test execution, 10 in the model construction, and 3 in the test criteria selection. Studies reported the test generator role 25 times, test executor role 20 times, and the monitor-coordinator of activities 12 times. Additional studies to understand the benefits of agent-oriented approaches for model-based testing are required.
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Acknowledgments
This work was partially supported by the University of Costa Rica No. 834-B8-A27. We thank the Empirical Software Engineering Group at UCR for the valuable feedback and help.
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Ramírez-Méndez, J., Quesada-López, C., Martínez, A., Jenkins, M. (2021). Agent-Oriented Approaches for Model-Based Software Testing: A Mapping Study. In: Rocha, Á., Ferrás, C., López-López, P.C., Guarda, T. (eds) Information Technology and Systems. ICITS 2021. Advances in Intelligent Systems and Computing, vol 1330. Springer, Cham. https://doi.org/10.1007/978-3-030-68285-9_33
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