Enabling the Collaborative Definition of DSMLs

International audienceSoftware development processes are collaborative in nature. Neglecting the key role of end-users leads to software that does not satisfy their needs. This collaboration becomes specially important when creating Domain-Specific Modeling Languages (DSMLs), which are (modeling) languages specifically designed to carry out the tasks of a particular domain. While end-users are actually the experts of the domain for which a DSML is developed, their participation in the DSML specification process is still rather limited nowadays. In this paper we propose a more community-aware language development process by enabling the active participation of all community members (both developers and end-users of the DSML) from the very beginning. Our proposal is based on a DSML itself, called Collaboro, which allows representing change proposals on the DSML design and discussing (and tracing back) possible solutions, comments and decisions arisen during the collaboration

Automated Model Merge by Design Space Exploration

Industrial applications of model-driven engineering to develop large and complex systems resulted in an increasing demand for collaboration features. However, use cases such as model differencing and merging have turned out to be a difficult challenge, due to (i) the graph-like nature of models, and (ii) the complexity of certain operations (e.g. hierarchy refactoring) that are common today. In the paper, we present a novel search-based automated model merge approach where rule-based design space exploration is used to search the space of solution candidates that represent conflict-free merged models. Our method also allows engineers to easily incorporate domain-specific knowledge into the merge process to provide better solutions. The merge process automatically calculates multiple merge candidates to be presented to domain experts for final selection. Furthermore, we propose to adopt a generic synthetic benchmark to carry out an initial scalability assessment for model merge with large models and large change sets

Hierarchical clustering of metamodels for comparative analysis and visualization

Many applications in Model-Driven Engineering involve processing multiple models or metamodels. A good example is the comparison and merging of metamodel variants into a common metamodel in domain model recovery. Although there are many sophisticated techniques to process the input dataset, little attention has been given to the initial data analysis, visualization and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. In this paper we present a generic approach for metamodel comparison, analysis and visualization as an exploratory first step for domain model recovery. We propose representing metamodels in a vector space model, and applying hierarchical clustering techniques to compare and visualize them as a tree structure. We demonstrate our approach on two Ecore datasets: a collection of 50 state machine metamodels extracted from GitHub as top search results; and ∼ \u3cbr/\u3e∼\u3cbr/\u3e100 metamodels from 16 different domains, obtained from AtlanMod Metamodel Zoo

A manifesto for semantic model differencing

Abstract. Models are heavily used in software engineering and together with their systems they evolve over time. Thus, managing their changes is an important challenge for system maintainability. Existing approaches to model differencing concentrate on heuristics matching between model syntactic level. While showing some success, these approaches are inherently limited to comparing syntactic structures. This paper is a manifesto for research on semantic model differencing. We present our vision to develop semantic diff operators for model comparisons: operators whose input consists of two models and whose output is a set of diff witnesses, instances of one model that are not instances of the other. In particular, if the models are syntactically different but there are no diff witnesses, the models are semantically equivalent. We demonstrate our vision using two concrete diff operators, for class diagrams and for activity diagrams. We motivate the use of semantic diff operators, briefly discuss the algorithms to compute them, list related challenges, and show their application and potential use as new fundamental building blocks for change management in model-driven engineering.