Abstract. MDE is being applied to the development of increasingly complex systems that require larger model transformations. Given that the specification of such transformations is an error-prone task, techniques to guarantee their quality must be provided. Testing is a wellknown technique for finding errors in programs. In this sense, adoption of testing techniques in the model transformation domain would be helpful to improve their quality. So far, testing of model transformations has focused on black-box testing techniques. Instead, in this paper we provide a white-box test model generation approach for ATL model transformations.
International audienceModel-Driven Engineering (MDE) is a software engineering paradigm where models play a key role. In a MDE-based development process, models are successively transformed into other models and eventually into the final source code by means of a chain of model transformations. Since writing model transformations is an error-prone task, mechanisms to ensure their reliability are greatly needed. One way of achieving this is by means of testing. A challenging aspect when testing model transformations is the generation of adequate input test data. Most existing approaches generate test data following a black-box approach based on some sort of partition analysis that exploits the structural features of the source metamodel of the transformation. However, these analyses pay no attention to the OCL invariants of the metamodel or do it very superficially. In this paper, we propose a mechanism that systematically analyzes OCL constraints in the source metamodel in order to fine-tune this partition analysis and therefore, the generation of input test data. Our mechanism can be used in isolation, or combined with other black-box or white-box test generation approaches
Applying traditional testing techniques to Cyber-Physical Systems (CPS) is challenging due to the deep intertwining of software and hardware, and the complex, continuous interactions between the system and its environment. To alleviate these challenges we propose to conduct testing at early stages and over executable models of the system and its environment. Model testing of CPSs is however not without difficulties. The complexity and heterogeneity of CPSs renders necessary the combination of different modeling formalisms to build faithful models of their different components. The execution of CPS models thus requires an execution framework supporting the co-simulation of different types of models, including models of the software (e.g., SysML), hardware (e.g., SysML or Simulink), and physical environment (e.g., Simulink). Furthermore, to enable testing in realistic conditions, the co-simulation process must be (1) fast, so that thousands of simulations can be conducted in practical time, (2) controllable, to precisely emulate the expected runtime behavior of the system and, (3) observable, by producing simulation data enabling the detection of failures. To tackle these challenges, we propose a SysML-based modeling methodology for model testing of CPSs, and an efficient SysML-Simulink co-simulation framework. Our approach was validated on a case study from the satellite domain.
CCS CONCEPTS• Software and its engineering → Software system models; Model-driven software engineering; Software verification and validation;
Abstract-Correctness of UML class diagrams annotated with OCL constraints can be checked using bounded verification techniques, e.g., SAT or constraint programming (CP) solvers. Bounded verification detects faults efficiently but, on the other hand, the absence of faults does not guarantee a correct behavior outside the bounded domain. Hence, choosing suitable bounds is a non-trivial process as there is a trade-off between the verification time (faster for smaller domains) and the confidence in the result (better for larger domains). Unfortunately, bounded verification tools provide little support in the bound selection process. In this paper, we present a technique that can be used to (i) automatically infer verification bounds whenever possible, (ii) tighten a set of bounds proposed by the user and (iii) guide the user in the bound selection process. This approach may increase the usability of UML/OCL bounded verification tools and improve the efficiency of the verification process.
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