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Using Model Learning for the Generation of Mock Components


Mocking objects is a common technique that substitutes parts of a program to simplify the test case development, to increase test coverage or to speed up performance. Today, mocks are almost exclusively used with object oriented programs. But mocks could offer the same benefits with communicating systems to make them more reliable. This paper proposes a model-based approach to help developers generate mocks for this kind of system, i.e. systems made up of components interacting with each other by data networks and whose communications can be monitored. The approach combines model learning to infer models from event logs, quality metric measurements to help chose the components that may be replaced by mocks, and mock generation and execution algorithms to reduce the mock development time. The approach has been implemented as a tool chain with which we performed experimentations to evaluate its benefits in terms of usability and efficiency.
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hal-03048336 , version 1 (09-12-2020)





Sébastien Salva, Elliott Blot. Using Model Learning for the Generation of Mock Components. Testing Software and Systems - 32nd IFIP WG 6.1 International Conference, ICTSS 2020, Naples, Italy, December 9-11, 2020, Proceedings, pp.3-19, 2020, ⟨10.1007/978-3-030-64881-7_1⟩. ⟨hal-03048336⟩
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