A systematic literature review of cross-domain model consistency checking by model management tools
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A systematic literature review of cross-domain model consistency checking by model management tools Weslley Torres1 · Mark G. J. van den Brand1 · Alexander Serebrenik1 Received: 24 September 2019 / Revised: 15 July 2020 / Accepted: 30 September 2020 © The Author(s) 2020
Abstract Objective The goal of this study is to identify gaps and challenges related to cross-domain model management focusing on consistency checking. Method We conducted a systematic literature review. We used the keyword-based search on Google Scholar, and we identified 618 potentially relevant studies; after applying inclusion and exclusion criteria, 96 papers were selected for further analysis. Results The main findings/contributions are: (i) a list of available tools used to support model management; (ii) 40% of the tools can provide consistency checking on models of different domains and 25% on models of the same domain, and 35% do not provide any consistency checking; (iii) available strategies to keep the consistency between models of different domains are not mature enough; (iv) most of the tools that provide consistency checking on models of different domains can only capture up to two inconsistency types; (v) the main challenges associated with tools that manage models on different domains are related to interoperability between tools and the consistency maintenance. Conclusion The results presented in this study can be used to guide new research on maintaining the consistency between models of different domains. Example of further research is to investigate how to capture the Behavioral and Refinement inconsistency types. This study also indicates that the tools should be improved in order to address, for example, more kinds of consistency check. Keywords Model management · Systems engineering · Model-based systems engineering
1 Introduction Inconsistencies can cause catastrophic events: e.g., the NASA unmanned MARS Climate Orbiter [101] was destroyed in 1999 due to use of inconsistent metric units by design teams, and Airbus had 6 billion dollar loss in 2006 due to use of inconsistent specifications in different versions of design tools [114]. Inconsistencies can be found in several stages of the system development life cycle. In earlier stages, when engineers are eliciting requirements, they might misunderstand the stakeholders’ needs. Thus, the stakeholders’ needs might be Communicated by Lionel Briand.
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Weslley Torres [email protected] Mark G. J. van den Brand [email protected] Alexander Serebrenik [email protected]
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Eindhoven University of Technology, Eindhoven, Netherlands
modeled wrongly, resulting in a product that does not match their expectations. Another inconsistency can arise when the models (e.g., class diagram, activity diagram) are correct but the software developers misunderstand them, resulting in a source code that does not represent the design intention. The crucial point here is that the earlier the inconsistency is found, the less it will cost [64] to fix the inconsistency. In the
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