Hybrid Approach for Metamodel and Model Co-evolution

Abstract

.Evolution is an inevitable aspect which affects metamodels. When metamodels evolve, model conformity may be broken. Model co-evolution is critical in model driven engineering to automatically adapt models to the newer versions of their metamodels. In this paper we discuss what can be done to transfer models between versions of a metamodel. For this purpose we introduce hybrid approach for model and metamodel co-evolution, that first uses matching between two metamodels to discover changes and then applied evolution operators to migrate models. In this proposal, migration of models is done automatically; except, for non resolvable changes, where assistance is proposed to the users in order to co-evolve their models to regain conformity.

Recommender systems in model-driven engineering

“…These approaches differ either in the applied technique to compute and rank the repairs, or in the application domain. In particular, IntellEdit [93] ranks quick fix solutions to model inconsistency problems according to the least-change principle; PAR-MOREL [11,53] determines the model repair actions based on the user preferences and on the experience gained from repairing under different personalisation settings; the diagram predicate framework (DPF) [108] and the approach by Nassar et al [91] implement repairs as transformation rules; DiaGen [82] represents models as hypergraphs and uses hypergraph patches to produce recommendations for repairing models; Refacola [130] uses constraint-based rules; BPMoQualAssess [60] provides guidelines to improve the actual value of quality metrics for business process models; B-repair [24] is specific to the B formal specification language and ranks the suggested repairs based on their estimated quality; Revision [98] tracks model inconsistencies to the editing action originating them in the model history and fixes this action to obtain a consistent model; MDSafe-Cer [87] detects missing information for supporting key evidence in process-based argumentations, and recommends how to resolve such deviations; ASIMOV [38] assists in the co-evolution of models and meta-models by proposing model co-evolution actions that a metamodeller must have defined previously; and Anguel et al [8] also tackle the co-evolution problem, but they automatically fix resolvable changes and recommend coevolution actions to deal with non-resolvable changes.…”

“…In turn, the tool by Nassar et al [91] permits repairing models either automatically or interactively. Three approaches (5.88%) provide semiautomated enactment of recommendations: two are co-evolution approaches [8,38] that automatically infer and apply a migration strategy, but the user may need to select between alternative solution steps, e.g. in the case of nonresolvable changes; the other corresponds to the proactive modelling approach in PME [99], where models are automatically modified according to the models' meta-model, and the user is only prompted if several optional modifications exist.…”

“…This is the case of ASIMOV [38], where the language designer specifies the migration assistance rules, and modellers use them to obtain recommendations for model migration. In contrast, the approach by Anguel et al [8] suggests a migration strategy based on meta-model matching and the use of logic programming. Some rule-based RSs target behavioural models.…”

“…These recommendations assist in repairing inconsistent models using a variety of techniques, such as rules [38,91,108], guidelines [60] or reinforcement learning [11,53]. Sometimes, model repair occurs on specific contexts, like meta-model/model co-evolution [8,38] or conflict resolution in model versioning [21]. There is less support to repair meta-models and OCL constraints [12,28], transformations [117][118][119], and models within code-generation activities [79].…”