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Test case generation for Symbolic Distributed System Models : Application to Trickle based IoT Protocol

Abstract : Distributed systems are composed of many distant subsystems. In order to achieve a common task, subsystems communicate both with the local environment by external messages and with other subsystems by internal messages through a communication network. In practice, distributed systems are likely to reveal many kinds of errors, so that we need to test them before reaching a certain level of confidence in them. However, testing distributed systems is complicated due to their intrinsic characteristics. Without global clocks, subsystems cannot synchronize messages, leading to non-deterministic situations.Model-Based Testing (MBT) aims at checking whether the behavior of a system under test (SUT) is consistent with its model, specifying expected behaviors. MBT is useful for two main steps: test case generation and verdict computation. In this thesis, we are mainly interested in the generation of test cases for distributed systems.To specify the desired behaviors, we use Timed Input Output Symbolic Transition Systems (TIOSTS), provided with symbolic execution techniques to derive behaviors of the distributed system. Moreover, we assume that in addition to external messages, a local test case observes internal messages received and sent by the co-localized subsystem. Our testing framework includes several steps: selecting a global test purpose using symbolic execution on the global system, projecting the global test purpose to obtain a local test purpose per subsystem, deriving unitary test case per subsystem. Then, test execution consists of executing local test cases by submitting data compatible following a local test purpose and computing a test verdict on the fly. Finally, we apply our testing framework to a case study issued from a protocol popular in the context of IoT.
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Ngo Minh Thang Nguyen. Test case generation for Symbolic Distributed System Models : Application to Trickle based IoT Protocol. Other. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLC092⟩. ⟨tel-02645881⟩

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