303 research outputs found
Adding Graph Transformation Concepts to UML's Constraint Language OCL
AbstractThe Object Constraint Language OCL is an integral part of UML, the Unified Modeling Language standard. It has been added to UML as a logic-based sublanguage for the definition of class invariants and pre-/postconditions of operations. OCL is rather similar to a subset of the graph transformation language PROGRES, the so-called path expressions. These path expressions are used for similar purposes as OCL. In contrast to OCL, path expressions support functional abstraction and offer additional operators for conditional iteration and transitive closure. Furthermore, PROGRES possesses a visual query sublanguage and is equipped with a precise semantics definition. Based on our experiences with the development of PROGRES a number of modifications and extensions of OCL are suggested as recommendations for its forthcoming version 2.0
Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars
Model synchronization, i.e., the task of restoring consistency between two
interrelated models after a model change, is a challenging task. Triple Graph
Grammars (TGGs) specify model consistency by means of rules that describe how
to create consistent pairs of models. These rules can be used to automatically
derive further rules, which describe how to propagate changes from one model to
the other or how to change one model in such a way that propagation is
guaranteed to be possible. Restricting model synchronization to these derived
rules, however, may lead to unnecessary deletion and recreation of model
elements during change propagation. This is inefficient and may cause
unnecessary information loss, i.e., when deleted elements contain information
that is not represented in the second model, this information cannot be
recovered easily. Short-cut rules have recently been developed to avoid
unnecessary information loss by reusing existing model elements. In this paper,
we show how to automatically derive (short-cut) repair rules from short-cut
rules to propagate changes such that information loss is avoided and model
synchronization is accelerated. The key ingredients of our rule-based model
synchronization process are these repair rules and an incremental pattern
matcher informing about suitable applications of them. We prove the termination
and the correctness of this synchronization process and discuss its
completeness. As a proof of concept, we have implemented this synchronization
process in eMoflon, a state-of-the-art model transformation tool with inherent
support of bidirectionality. Our evaluation shows that repair processes based
on (short-cut) repair rules have considerably decreased information loss and
improved performance compared to former model synchronization processes based
on TGGs.Comment: 33 pages, 20 figures, 3 table
Gray Box Coverage Criteria for Testing Graph Pattern Matching
Model transformations (MT) are a core building block of Model-Driven Engineering. The quality of MT specifications and implementations is vital to their success. The well-researched formal underpinning of graph transformation (GT) theory allows for proving quality-relevant properties and enables stringent implementations. Yet, in practice, MT implementations often depend on verification/validation techniques based on dynamic testing. This work presents a new gray box coverage approach for systematic testing of GT-based MT implementations and pattern specifications. The approach uses GT specifics and enforces systematic testing by examining variable binding and unbinding steps, thereby not making further assumptions about the underlying pattern matching algorithm. A family of coverage criteria is defined as temporal logic (LTL) formulae, and the effectiveness of concrete criteria in limiting the testing effort is examined by an example
Improved Conflict Detection for Graph Transformation with Attributes
In graph transformation, a conflict describes a situation where two
alternative transformations cannot be arbitrarily serialized. When enriching
graphs with attributes, existing conflict detection techniques typically report
a conflict whenever at least one of two transformations manipulates a shared
attribute. In this paper, we propose an improved, less conservative condition
for static conflict detection of graph transformation with attributes by
explicitly taking the semantics of the attribute operations into account. The
proposed technique is based on symbolic graphs, which extend the traditional
notion of graphs by logic formulas used for attribute handling. The approach is
proven complete, i.e., any potential conflict is guaranteed to be detected.Comment: In Proceedings GaM 2015, arXiv:1504.0244
Conflict Detection for Edits on Extended Feature Models using Symbolic Graph Transformation
Feature models are used to specify variability of user-configurable systems
as appearing, e.g., in software product lines. Software product lines are
supposed to be long-living and, therefore, have to continuously evolve over
time to meet ever-changing requirements. Evolution imposes changes to feature
models in terms of edit operations. Ensuring consistency of concurrent edits
requires appropriate conflict detection techniques. However, recent approaches
fail to handle crucial subtleties of extended feature models, namely
constraints mixing feature-tree patterns with first-order logic formulas over
non-Boolean feature attributes with potentially infinite value domains. In this
paper, we propose a novel conflict detection approach based on symbolic graph
transformation to facilitate concurrent edits on extended feature models. We
describe extended feature models formally with symbolic graphs and edit
operations with symbolic graph transformation rules combining graph patterns
with first-order logic formulas. The approach is implemented by combining
eMoflon with an SMT solver, and evaluated with respect to applicability.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857
Incremental Graph Pattern Matching: Data Structures and Initial Experiments
Despite the large variety of existing graph transformation tools, the implementation of their pattern matching engine typically follows the same principle. First a matching occurrence of the left-hand side of the graph transformation rule is searched by some graph pattern matching algorithm. Then potential negative application conditions are checked that might eliminate the previous occurrence. However, when a new transformation step is started, all the information on previous matchings is lost, and the complex graph pattern matching phase is restarted from scratch each time. In the paper, we present the foundational data structures and initial experiments for an incremental graph pattern matching engine which keeps track of existing matchings in an incremental way to reduce the execution time of graph pattern matching
Complex Attribute Manipulation in TGGs with Constraint-Based Programming Techniques
Model transformation plays a central role in Model-Driven Engineering (MDE) and providing bidirectional transformation languages 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 quite promising and successful.  Although TGGs provide adequate support for structural aspects via object  patterns in TGG rules, support for handling complex relationships between different attributes is still missing in current implementations.  For certain applications, such as bidirectional model-to-text transformations, being able to manipulate attributes via string manipulation or arithmetic operations in TGG rules is vital.  Our contribution in this paper is to formalize a TGG extension that provides a means for complex attribute manipulation in TGG rules.  Our extension is compatible with the existing TGG formalization, and retains the "single specification'' philosophy of TGGs
A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation
Communication networks form the backbone of our society. Topology control
algorithms optimize the topology of such communication networks. Due to the
importance of communication networks, a topology control algorithm should
guarantee certain required consistency properties (e.g., connectivity of the
topology), while achieving desired optimization properties (e.g., a bounded
number of neighbors). Real-world topologies are dynamic (e.g., because nodes
join, leave, or move within the network), which requires topology control
algorithms to operate in an incremental way, i.e., based on the recently
introduced modifications of a topology. Visual programming and specification
languages are a proven means for specifying the structure as well as
consistency and optimization properties of topologies. In this paper, we
present a novel methodology, based on a visual graph transformation and graph
constraint language, for developing incremental topology control algorithms
that are guaranteed to fulfill a set of specified consistency and optimization
constraints. More specifically, we model the possible modifications of a
topology control algorithm and the environment using graph transformation
rules, and we describe consistency and optimization properties using graph
constraints. On this basis, we apply and extend a well-known constructive
approach to derive refined graph transformation rules that preserve these graph
constraints. We apply our methodology to re-engineer an established topology
control algorithm, kTC, and evaluate it in a network simulation study to show
the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the
referenced journal articl
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