ESR 6: Scalable and Extensible Cloud-based Low-Code Model RepositoryArsene Indamutsa
University of L'Aquila (Italy)
Over the last few years, manyMDE technologies have been proposed for developing domain specific modelling languages, and for supporting a wide range of model management activities. While existing MDE technologies provide practitioners with facilities that can simplify and automate many steps of model-based development processes, empirical studies show that some barriers still exist for the wider adoption of MDE technologies. Among the main issues that currently hamper a wider adoption ofMDE are the following: _ the support for discovery and reuse of existing modelling artefacts is very limited. As a result, similar transformations and other model management tools often need to be developed from scratch, thus raising the upfront investment and compromising the productivity benefits of model-based processes. For instance, when modellers identify a need for a domain-specific modelling language, it is quite common to implement it from scratch instead of reusing already developed languages that might satisfy their requirements; _ modelling and model management tools are commonly distributed as software packages that need to be downloaded and installed on client machines, often on top of complex software development IDEs (e.g. Eclipse).
The objective of this project is to develop an extensible and scalable repository that can address the issues mentioned above in LCE contexts. During the project a set of core services will be developed to store and manage typical modelling artefacts and tools. Atop such services it will be possible to develop extensions adding new functionality to the repository (e.g., calculation of model metrics). Moreover, it will be possible to use all the services by means through a web interface and REST APIs that will permit to adopt the available model management tools as software-as-a-service. Finally, the repository will be also designed so to support machine learning techniques (e.g., collaborative filtering) with the goal of providing modellers with real-time recommendations.
The project will develop a community-based model repository able to manage the persistence and reuse of heterogeneous modelling artefacts (including models, metamodels, and model transformations). The repository will support advanced query mechanisms and will be extensible in order to add new functionality, e.g. remote calculation of model metrics, semantic model differencing, validation and composition of model transformations, and even automated clustering of the stored modeling artefacts. Based on our preliminary results in we expect to store in the repository by the end of the project thousands of real modeling artefacts (including model transformations, metamodels, and models) collected during the development of Lowcomote.
MDEForgeWL. Towards cloud-based discovery and composition of model management services. Arsene Indamutsa, Juri Di Rocco, Davide Di Ruscio, Alfonso Pierantonio, Oct. 2021. MODELS 2021. ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems
Cloud-based modeling in IoT domain. a survey, open challenges and opportunities. Jean Felicien Ihirwe, Arsene Indamutsa, Davide Di Ruscio, Silvia Mazzini, Alfonso Pierantonio, Oct. 2021. 2nd Low-code Workshop at the ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS 2021)
A Low-Code Development Environment to Orchestrate Model Management Services. Arsene Indamutsa, Davide Di Ruscio, Alfonso Pierantonio, Sep. 2021. IFIP International Conference on Advances in Production Management Systems
Supporting the understanding and comparison of low-code development platforms. Apurvanand Sahay, Arsene Indamutsa, Davide Di Ruscio, Alfonso Pierantonio, 2020. 46th Euromicro Conference on Software Engineering and Advanced Applications (SE2A 2020)