Evaluation of Kermeta for Solving Graph-based Problems

Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud we show that Kermeta is relatively suitable for solving three graph-based\ud problems. First, Kermeta allows the specification of generic model\ud transformations such as refactorings that we apply to different metamodels\ud including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud of Kermeta to the formal language Alloy using an inter-language model\ud transformation. Kermeta uses Alloy to generate recommendations for\ud completing partially specified models. Third, we show that the Kermeta\ud compiler achieves better execution time and memory performance compared\ud to similar graph-based approaches using a common case study. The\ud three solutions proposed for those graph-based problems and their\ud evaluation with Kermeta according to the criteria of genericity,\ud extensibility, and performance are the main contribution of the paper.\ud Another contribution is the comparison of these solutions with those\ud proposed by other graph-based tools

Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page

Generic Model Refactorings

Many modeling languages share some common concepts and principles. For example, Java, MOF, and UML share some aspects of the concepts\ud of classes, methods, attributes, and inheritance. However, model\ud transformations such as refactorings specified for a given language\ud cannot be readily reused for another language because their related\ud metamodels may be structurally different. Our aim is to enable a\ud flexible reuse of model transformations across various metamodels.\ud Thus, in this paper, we present an approach allowing the specification\ud of generic model transformations, in particular refactorings, so\ud that they can be applied to different metamodels. Our approach relies\ud on two mechanisms: (1) an adaptation based mainly on the weaving\ud of aspects; (2) the notion of model typing, an extension of object\ud typing in the model-oriented context. We validated our approach by\ud performing some experiments that consisted of specifying three well\ud known refactorings (Encapsulate Field, Move Method, and Pull Up Method)\ud and applying each of them onto three different metamodels (Java,\ud MOF, and UML)

Saying Hello World with UML-RSDS - A Solution to the 2011 Instructive Case

In this paper we apply the UML-RSDS notation and tools to the "Hello World" case studies and explain the underlying development process for this model transformation approach.Comment: In Proceedings TTC 2011, arXiv:1111.440

A Model-Driven approach for functional test case generation

Test phase is one of the most critical phases in software engineering life cycle to assure the final system quality. In this context, functional system test cases verify that the system under test fulfills its functional specification. Thus, these test cases are frequently designed from the different scenarios and alternatives depicted in functional requirements. The objective of this paper is to introduce a systematic process based on the Model-Driven paradigm to automate the generation of functional test cases from functional requirements. For this aim, a set of metamodels and transformations and also a specific language domain to use them is presented. The paper finishes stating learned lessons from the trenches as well as relevant future work and conclusions that draw new research lines in the test cases generation context.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-

Meta-model Pruning

Large and complex meta-models such as those of Uml and its profiles are growing due to modelling and inter-operability needs of numerous\ud stakeholders. The complexity of such meta-models has led to coining\ud of the term meta-muddle. Individual users often exercise only a small\ud view of a meta-muddle for tasks ranging from model creation to construction\ud of model transformations. What is the effective meta-model that represents\ud this view? We present a flexible meta-model pruning algorithm and\ud tool to extract effective meta-models from a meta-muddle. We use\ud the notion of model typing for meta-models to verify that the algorithm\ud generates a super-type of the large meta-model representing the meta-muddle.\ud This implies that all programs written using the effective meta-model\ud will work for the meta-muddle hence preserving backward compatibility.\ud All instances of the effective meta-model are also instances of the\ud meta-muddle. We illustrate how pruning the original Uml metamodel\ud produces different effective meta-models

Automatically Discovering Hidden Transformation Chaining Constraints

Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information

Engineering Object-Oriented Semantics Using Graph Transformations

In this paper we describe the application of the theory of graph transformations to the practise of language design. We have defined the semantics of a small but realistic object-oriented language (called TAAL) by mapping the language constructs to graphs and their operational semantics to graph transformation rules. In the process we establish a mapping between UML models and graphs. TAAL was developed for the purpose of this paper, as an extensive case study in engineering object-oriented language semantics using graph transformation. It incorporates the basic aspects of many commonly used object-oriented programming languages: apart from essential imperative programming constructs, it includes inheritance, object creation and method overriding. The language specification is based on a number of meta-models written in UML. Both the static and dynamic semantics are defined using graph rewriting rules. In the course of the case study, we have built an Eclipse plug-in that automatically transforms arbitrary TAAL programs into graphs, in a graph format readable by another tool. This second tool is called Groove, and it is able to execute graph transformations. By combining both tools we are able to visually simulate the execution of any TAAL program