Automated verification of model transformations based on visual contracts

The final publication is available at Springer via http://dx.doi.org/10.1007/s10515-012-0102-yModel-Driven Engineering promotes the use of models to conduct the different phases of the software development. In this way, models are transformed between different languages and notations until code is generated for the final application. Hence, the construction of correct Model-to-Model (M2M) transformations becomes a crucial aspect in this approach. Even though many languages and tools have been proposed to build and execute M2M transformations, there is scarce support to specify correctness requirements for such transformations in an implementation-independent way, i.e., irrespective of the actual transformation language used. In this paper we fill this gap by proposing a declarative language for the specification of visual contracts, enabling the verification of transformations defined with any transformation language. The verification is performed by compiling the contracts into QVT to detect disconformities of transformation results with respect to the contracts. As a proof of concept, we also report on a graphical modeling environment for the specification of contracts, and on its use for the verification of transformations in several case studies.This work has been funded by the Austrian Science Fund (FWF) under grant P21374-N13, the Spanish Ministry of Science under grants TIN2008-02081 and TIN2011-24139, and the R&D programme of the Madrid Region under project S2009/TIC-1650

Development of Transformations from Business Process Models to Implementations by Reuse

This paper presents an approach for developing transformations from business process models to implementations that facilitates reuse. A transformation is developed as a composition of three smaller tasks: pattern recognition, pattern realization and activity transformation. The approach allows one to reuse the definition and implementation of pattern recognition and pattern realization in the development of transformations targeting different business process modeling and implementation languages. In order to decouple pattern recognition and pattern realization, the approach includes a pattern language to represent the output of the pattern recognition task, which forms the input of the pattern realization task

Object-oriented Programming Laws for Annotated Java Programs

Object-oriented programming laws have been proposed in the context of languages that are not combined with a behavioral interface specification language (BISL). The strong dependence between source-code and interface specifications may cause a number of difficulties when transforming programs. In this paper we introduce a set of programming laws for object-oriented languages like Java combined with the Java Modeling Language (JML). The set of laws deals with object-oriented features taking into account their specifications. Some laws deal only with features of the specification language. These laws constitute a set of small transformations for the development of more elaborate ones like refactorings

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)

Towards a Unified Notation to Represent Model Transformation

In order to unify our internal exchange and communication about transformations, we propose TrML (Transformation Modeling Language), a unified UML notation to design model transformations. This proposal aims to reify the synthesis of existing notations dedicated to transformation modeling. TrML is independent from implementation details and could be adapted to several transformation engines. To let TrML run on top of existing engine, we transform TrML model to a model accepted by the engine. But, which language should we use for the first transformation? TrML, the targeted engine or another one? In this article we will describe how we bootstrap our new language on top of existing transformation engines

Pyramidal Recurrent Unit for Language Modeling

LSTMs are powerful tools for modeling contextual information, as evidenced by their success at the task of language modeling. However, modeling contexts in very high dimensional space can lead to poor generalizability. We introduce the Pyramidal Recurrent Unit (PRU), which enables learning representations in high dimensional space with more generalization power and fewer parameters. PRUs replace the linear transformation in LSTMs with more sophisticated interactions including pyramidal and grouped linear transformations. This architecture gives strong results on word-level language modeling while reducing the number of parameters significantly. In particular, PRU improves the perplexity of a recent state-of-the-art language model Merity et al. (2018) by up to 1.3 points while learning 15-20% fewer parameters. For similar number of model parameters, PRU outperforms all previous RNN models that exploit different gating mechanisms and transformations. We provide a detailed examination of the PRU and its behavior on the language modeling tasks. Our code is open-source and available at https://sacmehta.github.io/PRU/Comment: Accepted as a long paper in EMNLP 201

Well-formed Model Co-evolution with Customizable Model Migration

Model-driven engineering (MDE) is a software engineering discipline which focuses on models as the primary artifact of the software development process while programs are mainly generated by means of model-to-code transformations. In particular, modeling languages tailored to specific domains promise to increase the productivity and quality of software. Nevertheless due to e.g. evolving requirements, modeling languages evolve and existing models have to be migrated. Corresponding manual model migration is tedious and error-prone, therefore tools have been developed to (partly) automate this process. We follow the idea of considering such modeling language and model co-evolutions as related graph transformations ensuring a correct and unique typing of migrated models. In this paper, we present a general and formal construction of well-formed model migration schemes that are able to co-adapt any model of a given modeling language to a performed meta-model change. We show how appropriate model migration schemes can be constructed and discuss how they may be customized