Karsten Hölscher

On translating UML models into graph transformation systems

In this paper we present a concept of a rigorous approach that provides a formal semantics for a fundamental subset of UML. This semantics is derived by translating a given UML model into a graph transformation system, allowing modelers to actually execute their UML model. The graph transformation system comprises graph transformation rules and a working graph which represents the current state of the modeled system. In order to support UML models which use OCL, we introduce a specific graph transformation approach that incorporates full OCL in the common UML fashion. The considered UML subset is defined by means of a metamodel similar to the UML 1.5 metamodel. The concept of a system state that represents the state of the system at a specific point in time during execution is likewise introduced by means of a metamodel. The simulated system run is performed by applying graph transformation rules on the working graph. The approach has been implemented in a research prototype which allows the modeler to execute the specified model and to validate the basic aspects of the model in an early software development phase.

Animated simulation of integrated UML behavioral models based on graph transformation

This paper shows how integrated UML models combining class, object, use-case, collaboration and state diagrams can be animated in a domain-specific layout. The presented approach is based on graph transformation, i.e., UML model diagrams are translated to a graph transformation system and the behavior of the integrated model is simulated by applications of graph transformation rules. For model validation, users may prefer to see the behavior of selected model aspects as scenarios presented in the layout of the application domain. We propose to integrate animation views with the models graph transformation system. A prototypical validation system has been implemented recently supporting the automatic translation of a UML model into a graph transformation system, and the interactive execution and simulation of the model behavior. We sketch the tool interconnection to GenGED, a visual language environment which allows to enrich graph transformation systems for model simulation by features for animation.

From UML Models to Graph Transformation Systems

In this paper we present an approach that allows to validate properties of UML models. The approach is based on an integrated semantics for central parts of the UML. We formally cover UML use case, class, object, statechart, collaboration, and sequence diagrams. Additionally full OCL is supported in the common UML fashion. Our semantics is based on the translation of a UML model into a graph transformation system consisting of graph transformation rules and a working graph that represents the system state. By applying the rules on the working graph, the evolution of the modeled system is simulated.

Autonomous units and their semantics — the sequential case

In this paper, we introduce the notion of a community of autonomous units as a rule-based and graph-transformational device to model processes that run interactively but independently of each other in a common environment. The emphasis of the approach is laid on the study of the formal semantics of a community as a whole and of each of its member units separately. We concentrate on the sequential case where only one unit can act at a time and the rule applications of the involved units are interleaved with each other.

UML Interaction Diagrams: Correct Translation of Sequence Diagrams into Collaboration Diagrams

In this paper, the two types of UML interaction diagrams are considered. A translation of sequence diagrams into collaboration diagrams is constructed by means of graph transformation and shown correct.

Autonomous Units to Model Interacting Sequential and Parallel Processes

In this paper, we introduce the notion of a community of autonomous units as a rulebased and graph-transformational device to model processes that run interactively but independently of each other in a common environment. The main components of an autonomous unit are a set of rules, a control condition, and a goal. Every autonomous unit transforms graphs by applying its rules so that the control condition is satisfied. If the goal is reached the resulting transformation process is successful. A community contains a set of autonomous units, an initial environment specification, and an overall goal. In every transformation process of a community the autonomous units interact via their common environment. As an example, the game Ludo is modeled as a community of self-controlled players who interact on a common board. The emphasis of the presented approach is laid on the study of the formal semantics of a community as a whole and of each of its member units separately. In particular, a sequential as well as a parallel semantics is introduced, and communities with parallel semantics are compared with Petri nets, cellular automata, and multiagent systems.

Autonomous Units: Basic Concepts and Semantic Foundation

Today, most data processing systems and most logistic systems comprise various, possibly distributed, components. These components typically act autonomously, but they may also communicate and interact with each other, spontaneously linking up to form a network. These components do not necessarily need to be stationary. Sometimes they even move or are carried around. Although the components act autonomously, the task to be solved is handled by their interaction and the system as a whole. In this paper the concept of autonomous units for modeling such systems is proposed. Autonomous units form a community with a common environment, in which they act and which they transform. Autonomous units are based on rules, the applications of which yield changes in the environment. They are also equipped with an individual goal, which they try to accomplish by applying their rules. A control condition enables autonomous units to select at any time and in any situation the rule that should actually be applied from the set of all applicable rules.

Undecidable Control Conditions in Graph Transformation Units

Graph transformation units are an approach-independent concept for programming by applying rules and imported transformation units to graphs, starting in an initial and ending in a terminal graph. This transformation process has to obey a so-called control condition, i.e. the device to select how rules or imported transformation units are to be combined in the transformation process executed by the unit. While the other parts of a unit may simply be required to be computable, this is too restrictive for control conditions. In this paper, we show that the semantics of certain control conditions is in general undecidable already when a single imported transformation unit occurs in the condition, and discuss the consequences for programming with graph transformation units.

Autonomous Units for Communication-based Dynamic Scheduling

Logistics has to deal with dynamics and uncertainties. In order to cope with such problems we introduce a communication-based approach built on distributed autonomous systems. In this work graph transformation with autonomous units is used as a rule-based instantiation of multi-agent systems to model logistic systems. The approachwill be presented using a scenario taken from transport logistics. Here loads which have to be transported are queued in order of their arrival but scheduled for further transportation according to their own constraints. In the paper we propose a negotiation between loads and the respective truck based on payment of transportation rates.

Semantics of Visual Models in a Rule-based Setting

In this paper, some fundamental aspects of the semantics of rule-based systems are sketched and related to the semantics of visual models. A rule-based system comprises a set of rules and some control conditions including descriptions of initial and terminal configurations. Semantically, the rules specify a binary relation on configurations of some kind by means of rule applications which are restricted according to the control conditions. As visual models are usually represented by diagrams, graphs or similar configurations, the rule-based setting can be employed to provide visual models with semantics.