Software systems aren’t getting any less complex to design, implement, and maintain, which applies to both the numerous diverse components and the myriad of people involved in the development processes. Even a straightforward configuration of a database back-end and an object-oriented front-end tool requires coordination among database analysts, programmers, HCI people, and increasing involvement of domain experts and stakeholders. They each may prefer, and have different competencies in, certain specific design mechanisms; e.g., one may want EER for the database design, UML diagrams for the front-end app, and perhaps structured natural language sentences with SBVR or ORM for expressing the business rules. This requires multi-modal modelling in a plurality of paradigms. This would then need to be supported by hybrid tools that offer interoperability among those modelling languages, since such heterogeneity won’t go away any time soon, or ever.
It is far from trivial to have these people work together whilst maintaining their preferred view of a unified system’s design, let alone doing all this design in one system. In fact, there’s no such tool that can seamlessly render such varied models across multiple modelling languages whilst preserving the semantics. At best, there’s either only theory that aims to do that, or only a subset of the respective languages’ features, or a subset of the required combinations. Well, more precisely, until our efforts. We set out to fill this gap in functionality, both in a theoretically sound way and implemented as proof-of-concept to demonstrate its feasibility. The latest progress was recently published in the paper entitled A framework for interoperability with hybrid tools in the Journal of Intelligent Information Systems [1], in collaboration with Germán Braun and Pablo Fillottrani.
First, we propose the Framework for semantiC Interoperability of conceptual data modelling Languages, FaCIL, which serves as the core orchestration mechanism for hybrid modelling tools with relations between components and a workflow that uses them. At its centre, it has a metamodel that is used for the interchange between the various conceptual models represented in different languages and it has sets of rules to and from the metamodel (and at the metamodel level) to ensure the semantics is preserved when transforming a model in one language into a model in a different language and such that edits to one model automatically propagate correctly to the model in another language. In addition, thanks to the metamodel-based approach, logic-based reconstructions of the modelling languages also have become easier to manage, and so a path to automated reasoning is integrated in FaCIL as well.
This generic multi-modal modelling interoperability framework FaCIL was instantiated with a metamodel for UML Class Diagrams, EER, and ORM2 interoperability specifically [2] (introduced in 2015), called the KF metamodel [3] with its relevant rules (initial and implemented ones), an English controlled natural language, and a logic-based reconstruction into a fragment of OWL (orchestration graphically from the paper). This enables a range of different user interactions in the modelling process, of which an example of a possible workflow is shown in the following figure.
These theoretical foundations were implemented in the web-based crowd 2.0 tool (with source code). crowd 2.0 is the first hybrid tool of its kind, tying together all the pieces such that now, instead of partial or full manual model management of transformations and updates in multiple disparate tools, these tasks can be carried out automatically in one application and therewith also allow diverse developers and stakeholders to work from a shared single system.
We also describe a use case scenario for it – on Covid-19, as pretty much all of the work for this paper was done during the worse-than-today’s stage of the pandemic – that has lots of screenshots from the tool in action, both in the paper (starting here, with details halfway in this section) and more online.
Besides evaluating the framework with an instantiation, a proof-of-concept implementation of that instantiation, and a use case, it was also assessed against the reference framework for conceptual data modelling of Delcambre and co-authors [4] and shown to meet those requirements. Finally, crowd 2.0’s features were assessed against five relevant tools, considering the key requirements for hybrid tools, and shown to compare favourable against them (see Table 2 in the paper).
Distinct advantages can be summed up as follows, from those 26 pages of the paper, where the, in my opinion, most useful ones are underlined here, and the most promising ones to solve another set of related problems with conceptual data modelling (in one fell swoop!) in italics:
- One system for related tasks, including visual and text-based modelling in multiple modelling languages, automated transformations and update propagation between the models, as well as verification of the model on coherence and consistency.
- Any visual and text-based conceptual model interaction with the logic has to be maintained only in one place rather than for each conceptual modelling and controlled natural language separately;
- A controlled natural language can be specified on the KF metamodel elements so that it then can be applied throughout the models regardless the visual language and therewith eliminating duplicate work of re-specifications for each modelling language and fragment thereof;
- Any further model management, especially in the case of large models, such as abstraction and modularisation, can be specified either on the logic or on the KF metamodel in one place and propagate to other models accordingly, rather than re-inventing or reworking the algorithms for each language over and over again;
- The modular design of the framework allows for extensions of each component, including more variants of visual languages, more controlled languages in your natural language of choice, or different logic-based reconstructions.
Of course, more can be done to make it even better, but it is a milestone of sorts: research into the theoretical foundations of this particular line or research had commenced 10 years ago with the DST/MINCyT-funded bi-lateral project on ontology-driven unification of conceptual data modelling languages. Back then, we fantasised that, with more theory, we might get something like this sometime in the future. And we did.
References
[1] Germán Braun, Pablo Fillottrani, and C Maria Keet. A framework for interoperability with hybrid tools. Journal of Intelligent Information Systems, in print since 29 July 2022.
[2] Keet, C. M., & Fillottrani, P. R. (2015). An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering, 98, 30–53.
[3] Fillottrani, P.R., Keet, C.M. KF metamodel formalization. Technical Report, Arxiv.org http://arxiv.org/abs/1412.6545. Dec 19, 2014. 26p.
[4] Delcambre, L. M. L., Liddle, S. W., Pastor, O., & Storey, V. C. (2018). A reference framework for conceptual modeling. In: 37th International Conference on Conceptual Modeling (ER’18). LNCS. Springer, vol. 11157, 27–42.
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