Evaluating the Understandability and Expressiveness of Simulation Executable Models with Professionals


Large-scale and complex systems exhibit (i) dynamic structures and behaviors, (ii) several components/systems involved and (iii) multiple interoperability links. Such technologies have exposed limitations and fragilities on traditional software specification languages (such as UML and SySML), since those languages were designed to document single (not multiple interoperating) systems, which can further compromise the quality of the final product. In this context, Executable Models (ExM) technology, such as simulation models, models@runtime and executable UML, match these requirements by supporting engineers with visualization of the systems structures (still at design-time) and the ability to model their behaviors and interactions. However, we currently observe a decrease in the use of models and consequently ExM by software engineering professionals in the academy and industry and we claim that those professionals have not exhibited abilities to use ExM even in simpler scenarios. In this paper, we present the results of an exploratory study on the perceptions of those professionals regarding the use of ExM to solve problems in their current practice. 58 professionals were exposed to situations to solve problems using a specific type of ExM (DEVS simulation models), based on a survey research. Responses were quantitatively and qualitatively analyzed. Results reveal that executable languages still require advances to bring them even closer to the current software engineering practice and towards a larger adoption in the future.
Palavras-chave: executable models, complex systems, simulation
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LEBTAG, Bruno G.; TEIXEIRA, Paulo G.; SANTOS, Rodrigo P.; VIANA, Davi; GRACIANO NETO, Valdemar V.. Evaluating the Understandability and Expressiveness of Simulation Executable Models with Professionals. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 19. , 2020, São Luiz do Maranhão. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 111-120.