Johannes Schönböck

Automated verification of model transformations based on visual contracts

Model-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.

Reuse in model-to-model transformation languages: are we there yet?

In the area of model-driven engineering, model transformations are proposed as the technique to systematically manipulate models. For increasing development productivity as well as quality of model transformations, reuse mechanisms are indispensable. Although numerous mechanisms have been proposed, no systematic comparison exists, making it unclear, which reuse mechanisms may be best employed in a certain situation. Thus, this paper provides an in-depth comparison of reuse mechanisms in model-to-model transformation languages and categorizes them along their intended scope of application. Finally, current barriers and facilitators to model transformation reuse are discussed.

Surveying Rule Inheritance in Model-to-Model Transformation Languages

Model transformations play a significant role in Model-Driven Engineering. However, their reuse mechanisms have yet to receive much attention. In this paper, we propose a comparison framework for rule inheritance in model-to-model transformation languages, and provide an in-depth evaluation of prominent representatives of imperative, declarative and hybrid transformation languages. The framework provides criteria for comparison along orthogonal dimensions, covering static aspects, which indicate whether a set of inheriting transformation rules is well-formed at compile-time, and dynamic aspects, which describe how inheriting rules behave at run-time. The application of this framework to dedicated transformation languages shows that, while providing similar syntactical inheritance concepts, they exhibit different dynamic inheritance semantics and offer basic support for checking static inheritance semantics, only.

Reusing Model Transformations across Heterogeneous Metamodels

Model transformations are key enablers for multi-paradigm modeling. However, currently there is little support for reusing transformations in different contexts since they are tightly coupled to the metamodels they are defined upon, and hence reusing them for other metamodels becomes challenging. Inspired from generic programming, we proposed generic model-to-model transformations, which are defined over so-called metamodel concepts, which are later bound to specific metamodels. Nevertheless, the current binding mechanism lacks automated resolution support for recurring structural heterogeneities between metamodels. Therefore, based on a systematic classification of heterogeneities, we propose a flexible binding mechanism being able to automatically resolve recurring structural heterogeneities between metamodels. For this, the binding model is analyzed and required adaptors are automatically added to the transformation.

Fact or fiction --- reuse in rule-based model-to-model transformation languages

Model transformations are mostly developed from scratch. For increasing development productivity as well as quality of model transformations, reuse mechanisms are indispensable. Although numerous mechanisms have been proposed, no systematic comparison exists making it unclear, which reuse mechanisms may be best employed in a certain situation. Therefore, this paper provides an in-depth comparison of reuse mechanisms in rule-based model-to-model transformation languages and categorizes them along their intended scope of application. For this, a systematic comparison framework for reuse mechanisms is proposed to highlight commonalities as well as differences. Finally, current barriers to model transformation reuse are outlined.

A comparison of rule inheritance in model-to-model transformation languages

Although model transformations presumably play a major role in Model-Driven Engineering, reuse mechanisms such as inheritance have received little attention so far. In this paper, we propose a comparison framework for rule inheritance in declarative model-to-model transformation languages, and provide an in-depth evaluation of three prominent representatives thereof, namely ATL, ETL (declarative subsets thereof), and TGGs. The framework provides criteria for comparison along orthogonal dimensions, covering static aspects, which indicate whether a set of inheriting transformation rules is well-formed at compile-time, and dynamic aspects, which describe how inheriting rules behave at run-time. The application of this framework to dedicated transformation languages shows that, while providing similar syntactical inheritance concepts, they exhibit different dynamic inheritance semantics and offer basic support for checking static inheritance semantics, only.

User profile integration made easy: model-driven extraction and transformation of social network schemas

User profile integration from multiple social networks is indispensable for gaining a comprehensive view on users. Although current social networks provide access to user profile data via dedicated APIs, they fail to provide accurate schema information, which aggravates the integration of user profiles, and not least the adaptation of applications in the face of schema evolution. To alleviate these problems, this paper presents, firstly, a semi-automatic approach to extract schema information from instance data. Secondly, transformations of the derived schemas to different technical spaces are utilized, thereby allowing, amongst other benefits, the application of established integration tools and methods. Finally, as a case study, schemas are derived for Facebook, Google+, and LinkedIn. The resulting schemas are analyzed (i) for completeness and correctness according to the documentation, and (ii) for semantic overlaps and heterogeneities amongst each other, building the basis for future user profile integration.

Modeling Situation-Aware Ambient Assisted Living Systems for Eldercare

The development of ambient assisted living (AAL) systems, which are tailored to health or elder care, requires specific methods and tools. AAL systems make often use of wireless sensor networks, machine learning algorithms and sensory devices. Since wireless sensor networks and their sensors are inhomogeneous, it became apparent that such systems need to cope with different hardware platforms, different programming languages, unreliable wireless communication, energy constraints, data analysis algorithms, recognition of situations, and deployment options. Developers to date tend to use a bottom-up approach: hardware components dictate the development of AAL systems and thereby restrict the range of use cases that can be realized; domain experts by contrast would prefer a top-down approach and model the system’s functionality independently from the hardware platform. Currently available software development environments and tools do not adequately support domain experts and developers to accomplish these tasks efficiently. This paper presents methods that support domain experts in their top-down approach, as well as technically experienced developers in their bottom-up approach. The implemented tools enable a model-driven software development process (from platform-independent modeling to generating AAL application code) and thus facilitate programming AAL systems.

Gulliver-A Framework for Building Smart Speech-Based Applications

Speech recognition has matured over the past years to the point that companies can seriously consider its use. However, from a developers perspective we observe that speech input is rarely used in mobile application development, not even if it allowed users to work with their devices more flexibly. This stems partly from the fact that programming speech-enabled applications is tedious, because there is insufficient framework and tool support. This paper describes a component-based framework that uniformly supports development of multimodal applications on heterogeneous devices, ranging from laptop PCs to mobile phones. It especially focuses on distributed components (each performing a single step in speech recognition) to enable speech recognition on any type of device. Moreover, it describes how to develop and integrate different user interfaces for one application (voice-only, graphical-only, and multimodal) in a model-driven development approach, to minimize development and maintenance costs

Consistent co-evolution of models and transformations

Evolving metamodels are in the center of Model-Driven Engineering, necessitating the co-evolution of dependent artifacts like models and transformations. While model co-evolution has been extensively studied, transformation co-evolution has received less attention up to now. Current approaches for transformation co-evolution provide a fixed, restricted set of metamodel (MM) changes, only. Furthermore, composite changes are treated as monolithic units, which may lead to inconsistent co-evolution for overlapping atomic changes and prohibits extensibility. Finally, transformation co-evolution is considered in isolation, possibly inducing inconsistencies between model and transformation co-evolution. To overcome these limitations, we propose a complete set of atomic MM changes being able to describe arbitrary MM evolutions. Reusability and extensibility are supported by means of change composition, ensuring an intra-artifact consistent co-evolution. Furthermore, each change provides resolution actions for both, models and transformations, ensuring an inter-artifact consistent co-evolution. Based on our conceptual approach, a prototypical implementation is presented.