Innovations in Systems and Software Engineering · 2022 · 16 citations · 41 references
EngineeringSystems EngineeringExperimental DesignModel-based Systems EngineeringSystems ModelingComputational ModelingModel-driven DevelopmentModel-driven Software EngineeringSimulation MethodsSoftware ModelingSystem SimulationExperimental ReplicabilityExperimental Results InterpretationModeling And SimulationReplicabilityReplication Study DesignSimulink ModelsSystematic Literature ReviewSystems Engineering Processes
Abstract Research on novel tools for model-based development differs from a mere engineering task by not only developing a new tool, but by providing some form of evidence that it is effective. This is typically achieved by experimental evaluations. Following principles of good scientific practice, both the tool and the models used in the experiments should be made available along with a paper, aiming at the replicability of experimental results. We investigate to which degree recent research reporting on novel methods, techniques, or algorithms supporting model-based development with MATLAB/Simulink meets the requirements for replicability of experimental results. Our results from studying 65 research papers obtained through a systematic literature search are rather unsatisfactory. In a nutshell, we found that only 31% of the tools and 22% of the models used as experimental subjects are accessible. Given that both artifacts are needed for a replication study, only 9% of the tool evaluations presented in the examined papers can be classified to be replicable in principle. We found none of the experimental results presented in these papers to be fully replicable, and 6% partially replicable. Given that tools are still being listed among the major obstacles of a more widespread adoption of model-based principles in practice, we see this as an alarming signal. While we are convinced that this situation can only be improved as a community effort, this paper is meant to serve as starting point for discussion, based on the lessons learnt from our study.
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The FAIR Guiding Principles for scientific data management and stewardship
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg et al. · Scientific Data · 2016 · 17.8K citations · Full text
Michael Gusenbauer, Neal Haddaway · Research Synthesis Methods · 2019 · 2.1K citations · Full text
Computing, Computer Science, Comparative Effectiveness Research +16