Johannes Schoenboeck

Surviving the heterogeneity jungle with composite mapping operators

Model transformations play a key role in the vision of Model-Driven Engineering. Nevertheless, mechanisms like abstraction, variation and composition for specifying and applying reusable model transformations - like urgently needed for resolving recurring structural heterogeneities - are insufficiently supported so far. Therefore, we propose to specify model transformations by a set of pre-defined mapping operators (MOps), each resolving a certain kind of structural heterogeneity. Firstly, these MOps can be used in the context of arbitrary metamodels since they abstract from concrete metamodel types. Secondly, MOps can be tailored to resolve certain structural heterogeneities by means of black-box reuse. Thirdly, based on a systematic set of kernel MOps resolving basic heterogeneities, composite ones can be built in order to deal with more complex scenarios. Finally, an extensible library of MOps is proposed, allowing for automatically executable mapping specifications since every MOp exhibits a clearly defined operational semantics.

Catch me if you can – debugging support for model transformations

Model-Driven Engineering places models as first-class artifacts throughout the software lifecycle requiring the availability of proper transformation languages. Although numerous approaches are available, they lack convenient facilities for supporting debugging and understanding of the transformation logic. This is because execution engines operate on a low level of abstraction, hide the operational semantics of a transformation, scatter metamodels, models, transformation logic, and trace information across different artifacts, and provide limited verification support. To tackle these problems, we propose a Domain-Specific Language (DSL) on top of Colored Petri Nets (CPNs)—called Transformation Nets—for the execution and debugging of model transformations on a high level of abstraction. This formalism makes the afore hidden operational semantics explicit by providing a runtime model in terms of places, transitions and tokens, integrating all artifacts involved into a homogenous view. Moreover, the formal underpinnings of CPNs enable comprehensive verification of model transformations.

A Petri Net Based Debugging Environment for QVT Relations

In the Model-Driven Architecture (MDA) paradigm the Query/View/Transformation (QVT) standard plays a vital role for model transformations. Especially the high-level declarative QVT Relations language, however, has not yet gained widespread use in practice. This is not least due to missing tool support in general and inadequate debugging support in particular. Transformation engines interpreting QVT Relations operate on a low level of abstraction, hide the operational semantics of a transformation and scatter metamodels, models, QVT code, and trace information across different artifacts. We therefore propose a model-based debugger representing QVT Relations on bases of TROPIC, a model transformation language utilizing a variant of Colored Petri Nets (CPNs). As a prerequisite for convenient debugging, TROPIC provides a homogeneous view on all artifacts of a transformation on basis of a single formalism. Besides that, this formalism also provides a runtime model, thus making the afore hidden operational semantics of the transformation explicit. Using an explicit runtime model allows to employ model-based techniques for debugging, e.g., using the Object Constraint Language (OCL) for simply defining breakpoints and querying the execution state of a transformation.

Towards an expressivity benchmark for mappings based on a systematic classification of heterogeneities

A crucial prerequisite for the success of Model Driven Engineering (MDE) is the seamless exchange of models between different modeling tools demanding for mappings between tool-specific metamodels. Thereby the resolution of heterogeneities between these tool-specific metamodels is a ubiquitous problem representing the key challenge. Nevertheless, there is no comprehensive classification of potential heterogeneities available in the domain of MDE. This hinders the specification of a comprehensive benchmark explicating requirements wrt. expressivity of mapping tools, which provide reusable components for resolving these heterogeneities. Therefore, we propose a feature-based classification of heterogeneities, which accordingly adapts and extends existing classifications. This feature-based classification builds the basis for a mapping benchmark, thereby providing a comprehensive set of requirements concerning expressivity of dedicated mapping tools. In this paper a first set of benchmark examples is presented by means of metamodels and conforming models acting as an evaluation suite for mapping tools.

Reviving QVT Relations: Model-Based Debugging Using Colored Petri Nets

The standardized QVT Relations language, one cornerstone of Model-Driven Architecture (MDA), has not yet gained widespread use in practice, not least due to missing tool support in general and inadequate debugging support in particular. Transformation engines interpreting QVT Relations operate on a low level of abstraction, hide the operational semantics of a transformation and scatter metamodels, models, QVT code, and traces across different artifacts. We propose a model-based debugger representing QVT Relations on bases of TROPIC, a model transformation framework which utilizes a variant of Colored Petri Nets (CPNs) providing an explicit runtime model and a homogenous view on all artifacts of a transformation.

An Engineering Toolbox to Build Situation Aware Ambient Assisted Living Systems

Due to increasing anticipated average life and health expenditure ambient assisted living (AAL) systems attract the attention of researchers. To successfully build and deploy AAL systems knowledge from different fields of computer science is needed: pervasive computing to gain the raw data, machine learning and pattern recognition to interpret these data and HCI knowledge to allow implicit interaction with the system.In this paper we propose a reference architecture for building AAL systems. Based on this reference architecture we introduce a toolbox that simplifies the development of AAL systems. The toolbox consists of a meta-model for pipeline systems, a low-level context model, high-level context ontologies, customizable components and tool support.

Modeling Distributed Signal Processing Applications

Wireless Sensor Networks in general and Body Sensor Networks in particular enable sophisticated applications in pervasive healthcare, sports training and other domains,where interconnected nodes work together. Their main goal is to derive context from raw sensor data with feature extraction and classification algorithms. Body sensor networks not only comprise a single sensor type or family but demand different hardware platforms, e.g., sensors to measure acceleration or blood-pressure, or tiny mobile devices to communicate with the user. The problem arises how to efficiently deal with these heterogeneous platforms and programming languages. This paper presents a distributed signal processing framework based on TinyOS and nesC. The framework forms the basis for a Model-Driven Software Development approach. By raising the level of abstraction formal models hide implementation specifics of the framework in a Platform Specific Model. A Platform Independent Model further lifts modeling to functional and non-functional requirements independent from platforms. Thereby we promote cooperation between domain experts and software engineers and facilitate reusability of applications across different platforms.

From the heterogeneity jungle to systematic benchmarking

One of the key challenges in the development of model transformations is the resolution of recurring semantic and syntactic heterogeneities. Thus, we provide a systematic classification of heterogeneities building upon a feature model that makes the interconnections between them explicit. On the basis of this classification, a set of benchmark examples was derived and used to evaluate current approaches to the specification of model transformations. We found, that approaches on the conceptual level lack expressivity whereas execution level approaches lack support for reuse. Moreover, only few of the approaches evaluated provide key features such as an automatic trace model or the ability to reuse specifications by inheritance.

Plug & play model transformations: a DSL for resolving structural metamodel heterogeneities

Model transformations play a key role in the vision of Model-Driven Engineering. Thereby, the resolution of structural heterogeneities between metamodels (MMs) represents the key challenge. For this task, current approaches require the definition of partly tricky, low-level recurring transformation logic but neglect to offer reusable components. Moreover, little attention has been paid to heterogeneities caused by the concept of inheritance, although extensively used in MMs. Therefore, we propose to specify model transformations in a plug & and play manner by a set of pre-defined mapping operators (MOps) representing a DSL to resolve structural heterogeneities. For coping with inheritance in MMs, we introduce an inheritance mechanism between MOps allowing to reuse parts of the mapping definitions. Moreover, dedicated MOps for resolving heterogeneities when one MM comprises inheritance hierarchies whereas the other one does not are presented, which are well-known problems in object-relational transformations and object-oriented refactorings.

TROPIC: a framework for model transformations on petri nets in color

Model transformation languages, the cornerstone of Model-Driven Engineering, often lack mechanisms for abstraction, reuse and debugging. We propose a model transformation framework providing different abstraction levels together with an extensible library of predefined transformations and a dedicated runtime model in terms of Coloured Petri Nets for transformation execution and debugging.