Jan Rieke

A Survey of Triple Graph Grammar Tools

Model transformation plays a central role in Model-Driven Engineer- ing (MDE) and supporting bidirectionality is a current challenge with important applications. Triple Graph Grammars (TGGs) are a formally founded, bidirectional model transformation language shown by numerous case studies to be promising and useful in practice. TGGs have been researched for more than 15 years and multiple TGG tools are under active development. Although a common theoreti- cal foundation is shared, TGG tools differ considerably concerning expressiveness, applicability, efficiency, and the underlying translation algorithm. There currently exists neither a quantitative nor a qualitative overview and comparison of TGG tools and it is quite difficult to understand the different foci and corresponding strengths and weaknesses. Our contribution in this paper is to develop a set of criteria for com- paring TGG tools and to provide a concrete quantitative and qualitative comparison of three TGG tools.

A Comparison of Incremental Triple Graph Grammar Tools

Triple Graph Grammars (TGGs) are a graph-based and visual technique for specifying bidirectional model transformation. TGGs can be used to transform models from scratch (in the batch mode), but the real potential of TGGs lies in propagating updates incrementally. Existing TGG tools differ considerably in their incremental mode concerning underlying algorithms, user-oriented aspects, incremental update capabilities, and formal properties. Indeed, the different foci, strengths, and weaknesses of current TGG tools in the incremental mode are difficult to discern, especially for non-developers. In this paper, we close this gap by (i) identifying a set of criteria for a qualitative comparison of TGG tools in the incremental mode, (ii) comparing three prominent incremental TGG tools with regard to these criteria, and (iii) conducting a quantitative comparison by means of runtime measurements.

Applying advanced TGG concepts for a complex transformation of sequence diagram specifications to timed game automata

Declarative model transformation languages like QVT-R and TGGs are particularly convenient because mappings between models can be specified in a rule-based way, describing how patterns in one model correspond to patterns in another. The same mapping specification can be used for different transformation and synchronization scenarios, which are important in model-based software engineering. However, even though these languages already exist for a while, they are not widely used in practice today. One reason for that is that these languages often do not provide sufficiently rich features to cope with many problems that occur in practice. We report on a complex model transformation that we have solved by TGGs. We present advanced extensions of the TGG language that we have integrated in our tool, the TGG Interpreter.

Simulating Self-Adaptive Component-Based Systems Using MATLAB/Simulink

The automotive industry uses MATLAB/Simulink models for specifying the behavior of software components and for early validation of that behavior using model-in-the-loop simulations. During a simulation run, these models may not structurally change. Thus, MATLAB/Simulink is not amenable to realizing self-adaption behavior, where the software architecture of the system evolves during runtime. In this paper, we show how to model self-adaptive software using our language Mechatronic UML and how we transform a model specified in Mechatronic UML into a MATLAB/Simulink model automatically. In particular, we generate several helper functions that emulate self-adaptive behavior in MATLAB/Simulink, relying only on standard Simulink blocks. We illustrate our approach using an example of car platoons.

The Mechatronic UML Development Process

The advanced functions of mechatronic systems today are essentially realized by software that controls complex processes and enables the communication and coordination of multiple system components. We have developed Mechatronic UML, a comprehensive technique for the model-based development of hybrid real-time component-based systems. Mechatronic UML is based on a well-defined subset of UML diagrams, formal analysis and composition methods. Vital for the successful development with Mechatronic UML, however, is a systematic development process, on which we report in this paper.

Methods for the Design and Development

After the domain-spanning conceptual design, engineers from different domains work in parallel and apply their domain-specific methods and modeling languages to design the system. Vital for the successful design, are system optimization methods and the design of the reconfiguration behavior. The former methods enable the parametric adaption of the system’s behavior, e.g. an adaption of controller parameters, according to a current selection of the system’s objectives. The latter realizes structural adaption of the system’s behavior, e.g. the exchange of software or hardware parts. Altogether, this leads to a complex system behavior that is hard to overview. In addition, self-optimizing systems are used in safety-critical environments. Consequently, the system’s safety-critical behavior has to undergo a rigorous verification and testing process. Existing design methods do not address all of these challenges together. Indeed, a combination of established design methods for traditional technical systems with novel methods that focus on these challenges is necessary. In this chapter, we will focus on such new methods. We will introduce new system optimization and design methods to develop reconfigurations of the software and the microelectronics. In order to ensure the correctness of safety-critical functionality, we propose new testing methods and formal methods to ensure safety-properties of the software. We show how to apply virtual prototyping to deal with the complexity of self-optimizing systems and perform an early analysis of the overall system. As each domain applies its own modeling languages, the result of these methods are several overlapping models. In order to keep these domain-specific models consistent among all domains, we will introduce a new semi-automatic model synchronization technique. Each of these design methods are integrated with the reference process for the development of self-optimizing systems.