Uncertainty management with extra-functional qualities in multi-artefact co-evolution

By: Francesco Basciani, Davide Di Ruscio, Ludovico Iovino, Alfonso Pierantonio

Abstract

Metamodels are at the core of any model-driven projects and give the experts a linguistic means to abstractly represent the problem domain’s instances. Metamodels are prone to modifications due to new insights emerging from the domain, improvements, and features being added to the modeling language over time. Metamodel evolution may induce severe repercussions over the related artifacts, which might need to be consistently adapted. The co-evolution problem poses another issue related to the epistemic uncertainty arising as a response to a metamodel change where many different consistency restoration procedures are possible, e.g., restoring models and transformations. In this work we rely on the notion of information loss (IL) to understand which migration combinations are preferable to others in coupled evolution, by offering a ranking of the possible solutions. The IL denotes that part of the knowledge contained in the source models cannot be conveyed or translated into the target models, using a selected transformation, resulting in a loss of information. Such aspect represents an extra-functional quality that can be used for ranking the selection of certain transformation migrations in the context of multiple available alternatives and in combination with model migrations. Information loss induced by a (migrated) transformation can be defined as the amount of information lost executing the (migrated) transformation compared to the original output model obtained by executing the original transformation. The proposed approach is supported by a prototype tool that we demonstrate and validate.

Keywords

MDE , Parallel Coupled-evolution , Information loss.

Cite as:

Francesco Basciani, Davide Di Ruscio, Ludovico Iovino, Alfonso Pierantonio, “Uncertainty management with extra-functional qualities in multi-artefact co-evolution”, Journal of Object Technology, Volume 20, no. 3 (June 2021), pp. 2:1-15, doi:10.5381/jot.2021.20.3.a2.