Stephan Hildebrandt

Model synchronization at work: keeping SysML and AUTOSAR models consistent

During the overall development of complex engineering systems different modeling notations are employed. For example, in the domain of automotive systems system engineering models are employed quite early to capture the requirements and basic structuring of the entire system, while software engineering models are used later on to describe the concrete software architecture. Each model helps in addressing the specific design issue with appropriate notations and at a suitable level of abstraction. However, when we step forward from system design to the software design, the engineers have to ensure that all decisions captured in the system design model are correctly transferred to the software engineering model. Even worse, when changes occur later on in either model, today the consistency has to be reestablished in a cumbersome manual step. In this paper, we present how model synchronization and consistency rules can be applied to automate this task and ensure that the different models are kept consistent. We also introduce a general approach for model synchronization. Besides synchronization, the approach consists of tool adapters as well as consistency rules covering the overlap between the synchronized parts of a model and the rest. We present the model synchronization algorithm based on triple graph grammars in detail and further exemplify the general approach by means of a model synchronization solution between system engineering models in SysML and software engineering models in AUTOSAR which has been developed for an industrial partner.

Improved Flexibility and Scalability by Interpreting Story Diagrams

In this paper, we present an interpreter for Story Diagrams working on Eclipse Modeling Framework (EMF) models. The interpreter provides a more flexible and, under certain circumstances, a more scalable solution than the compiled Java code generated from Story Diagrams by Fujaba. of Dynamic EMF even allows the evolution of meta models at runtime. Story Diagrams can now be modeled and executed within Eclipse. They can be modified and re-executed by the Story Diagram interpreter immediately without recompiling the source code and restarting the application. Our implementation also supports higher-order transformations by using Story Diagrams to modify other Story Diagrams. generation is not applicable, like running systems. While interpretation obviously results in performance drawbacks, we demonstrate that the Story Diagram interpreter is able to improve the performance in certain worst-case situations compared to the average generated code. This is achieved by a dynamic ordering of the matching process, which considers the actual number of elements in an association at runtime. Such a dynamic ordering can minimize the matching effort considerably. In contrast, Fujaba generated code uses a static matching strategy. Whereas the Fujaba Story Diagrams have potentially high performance fluctuations, the performance of the Story Diagram interpreter is steadier and more scalable compared to the generated Java code.

Incremental model synchronization for efficient run-time monitoring

The model-driven engineering community has developed expressive model transformation techniques based on metamodels, which ease the specification of translations between different model types. Thus, it is attractive to also apply these techniques for autonomic and self-adaptive systems at run-time to enable a comprehensive monitoring of their architectures while reducing development efforts. This requires special solutions for model transformation techniques as they are applied at run-time instead of their traditional usage at development time. In this paper we present an approach to ease the development of architectural monitoring based on incremental model synchronization with triple graph grammars. We show that the provided incremental synchronization between a running system and models for different self-management capabilities provides a significantly better compromise between performance and development costs than manually developed solutions.

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.

Bridging the gap between formal semantics and implementation of triple graph grammars

The correctness of model transformations is a crucial element for model-driven engineering of high-quality software. A prerequisite to verify model transformations at the level of the model transformation specification is that an unambiguous formal semantics exists and that the implementation of the model transformation language adheres to this semantics. However, for existing relational model transformation approaches, it is usually not really clear under which constraints particular implementations really conform to the formal semantics. In this paper, we will bridge this gap for the formal semantics of triple graph grammars (TGG) and an existing efficient implementation. While the formal semantics assumes backtracking and ignores non-determinism, practical implementations do not support backtracking, require rule sets that ensure determinism, and include further optimizations. Therefore, we capture how the considered TGG implementation realizes the transformation by means of operational rules, define required criteria, and show conformance to the formal semantics if these criteria are fulfilled. We further outline how static and runtime checks can be employed to guarantee these criteria.

Incremental model synchronization for multiple updates

One required capability to make the vision of model-driven software development reality is, that changes in the models can be consistently propagated between the different related models. In this paper, we present how our model synchronization algorithm based on triple graph grammars, presented in [11], can be improved, such that it outperforms batch processing also in case of multiple updates. We present an evaluation of our improvement, demonstrating that we can keep the speedup for the incremental processing in the average case for smaller numbers of changes, while accelerating the synchronization for larger numbers of changes. The improved algorithm is at least as fast as the batch algorithm.

Model-driven architectural monitoring and adaptation for autonomic systems

Architectural monitoring and adaptation allows self-management capabilities of autonomic systems to realize more powerful adaptation steps, which observe and adjust not only parameters but also the software architecture. However, monitoring as well as adaptation of the architecture of a running system in addition to the parameters are considerably more complex and only rather limited and costly solutions are available today. In this paper we propose a model-driven approach to ease the development of architectural monitoring and adaptation for autonomic systems. Using meta models and model transformation techniques, we were able to realize an incremental synchronization between the run-time system and models for different self-management activities. The synchronization might be triggered when needed and therefore the activities can operate concurrently.

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.

Automatic conformance testing of optimized triple graph grammar implementations

In model-driven development, model transformations can be specified using an operational (imperative) or relational (declarative) approach. When using a relational approach, approved formal concepts are necessary to derive a conform operationalization. In general, though, it is not sure if implementations realize these formal concepts in an entirely correct way. Moreover, usually, available formal concepts neither cover every technicality, nor cover each additional optimization an implementation relies on. Consequently, conformance needs to be validated also on the implementation level. Conformance means that for each source model S and target model T related according to the relational specification, a corresponding implementation transforms S into T (and T into S in case that the specification is bidirectional). We present an automatic conformance testing approach for TGG implementations, where the Triple Graph Grammar (TGG) approach is an important representative of relational model transformation approaches. We show that the grammar character of TGGs is very convenient for the automatic generation of conformance test cases. In particular, test input models can be generated together with their expected result obtaining a complete oracle. We show how to measure test suite quality and evaluate our approach on our own TGG implementation.

Graph transformations for MDE, adaptation, and models at runtime

Software evolution and the resulting need to continuously adapt the software is one of the main challenges for software engineering. The model-driven development movement therefore aims at improving the longevity of software by keeping the development artifacts more consistent and better changeable by employing models and to a certain degree automated model operations. Another trend are systems that tackle the challenge at runtime by being able to adapt their structure and behavior to be more flexible and operate in more dynamic environments (e.g., context-aware software, autonomic computing, self-adaptive software). Finally, models at runtime, where the benefits of model-driven development are employed at runtime to support adaptation capabilities, today lead towards a unification of both ideas. In this paper, we present graph transformations and show that they can be employed to engineer solutions for all three outlined cases. Furthermore, we will even be able to demonstrate that graph transformation based technology has the potential to also unify all three cases in a single scenario where models at runtime and runtime adaptation is linked with classical MDE. Therefore, we at first provide an introduction in graph transformations, then present the related techniques of Story Pattern and Triple Graph Grammars, and demonstrate how with the help of both techniques model transformations, adaptation behavior and runtime model framework work. In addition, we show that due to the formal underpinning analysis becomes possible and report about a number of successful examples.