Property-Aware Unit Testing of UML-RT Models in the Context of MDE

Ahmadi, Reza; Hili, Nicolas; Dingel, Juergen

doi:10.1007/978-3-319-92997-2_10

Cited by 3 publications

(2 citation statements)

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“…We consider as a running example a sample executable DSL aiming at easing the modeling and early simulation of Arduino boards along their sketches. Figure 1 shows an excerpt of the definition of such Arduino xDSL 3 , which is further introduced in the following sections. From now on, we refer to this xDSL as xArduino.…”

Section: Running Example: Arduinomentioning

confidence: 99%

“…For example, Frolich and Link [18] generate test cases from Statecharts by translating the Statecharts into a planning problem, and using a planning tool to find test cases as solutions to the problem. Ahmadi and Hili [3] present an approach for automatically test components of UML-RT models with respect to a set of properties defined by state machines, and apply slicing to reduce the size of the components with respect to the properties. Rocha et al [42] generate JUnit test cases from sequence diagrams via a transformation of the latter into extended finite state machines.…”

Section: Test Case Generation For Behavioral Modelsmentioning

confidence: 99%

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Automatic test amplification for executable models

Khorram

Bousse

Mottu

et al. 2022

Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems

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Behavioral models are important assets that must be thoroughly verified early in the design process. This can be achieved with manuallywritten test cases that embed carefully hand-picked domain-specific input data. However, such test cases may not always reach the desired level of quality, such as high coverage or being able to localize faults efficiently. Test amplification is an interesting emergent approach to improve a test suite by automatically generating new test cases out of existing manually-written ones. Yet, while ad-hoc test amplification solutions have been proposed for a few programming languages, no solution currently exists for amplifying the test cases of behavioral models.In this paper, we fill this gap with an automated and generic approach. Given an executable DSL, a conforming behavioral model, and an existing test suite, our approach generates new regression test cases in three steps: (i) generating new test inputs by applying a set of generic modifiers on the existing test inputs; (ii) running the model under test with new inputs and generating assertions from the execution traces; and (iii) selecting the new test cases that increase the mutation score. We provide tool support for the approach atop the Eclipse GEMOC Studio 1 and show its applicability in an empirical study. In the experiment, we applied the approach to 71 test suites written for models conforming to two different DSLs, and for 67 of the 71 cases, it successfully improved the mutation score between 3.17 % and 54.11 % depending on the initial setup. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Model-driven software engineering.

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Section: Running Example: Arduinomentioning

confidence: 99%