Alternative Effort-optimal Model-based Strategy for State Machine Testing of IoT Systems
This work addresses testing efficiency for IoT system developers, but it is incremental as it builds on existing model-based testing methods.
The paper tackles the problem of generating test cases for IoT systems with state machine characteristics by proposing a novel model-based testing strategy that allows flexible adjustment of test case length and start/end states, resulting in fewer, shorter test cases with less duplication compared to an N-switch coverage-based approach.
To effectively test parts of the Internet of Things (IoT) systems with a state machine character, Model-based Testing (MBT) approach can be taken. In MBT, a system model is created, and test cases are generated automatically from the model, and a number of current strategies exist. In this paper, we propose a novel alternative strategy that concurrently allows us to flexibly adjust the preferred length of the generated test cases, as well as to mark the states, in which the test case can start and end. Compared with an intuitive N-switch coverage-based strategy that aims at the same goals, our proposal generates a lower number of shorter test cases with fewer test step duplications.