Various kinds of typed attributed graphs are used to represent states of systems from a broad range of domains. For dynamic systems, established formalisms such as graph transformations provide a formal model for defining state sequences. We consider the extended case where time elapses between states and introduce a logic to reason about these sequences. With this logic we express properties on the structure and attributes of states as well as on the temporal occurrence of states that are related by their inner structure, which no formal logic over graphs accomplishes concisely so far. Firstly, we introduce graphs with history by equipping every graph element with the timestamp of its creation and, if applicable, its deletion. Secondly, we define a logic on graphs by integrating the temporal operator until into the well-established logic of nested graph conditions. Thirdly, we prove that our logic is equally expressive to nested graph conditions by providing a suitable reduction. Finally, the implementation of this reduction allows for the tool-based analysis of metric temporal properties for state sequences. Verschiedene Arten von getypten attributierten Graphen werden benutzt, um Zustände von Systemen in vielen unterschiedlichen Anwendungsbereichen zu beschreiben. Der etablierte Formalismus der Graphtransformationen bietet ein formales Model, um Zustandssequenzen für dynamische Systeme zu definieren. Wir betrachten den erweiterten Fall von solchen Sequenzen, in dem Zeit zwischen zwei verschiedenen Systemzuständen vergeht, und führen eine Logik ein, um solche Sequenzen zu beschreiben. Mit dieser Logik drücken wir zum einen Eigenschaften über die Struktur und die Attribute von Zuständen aus und beschreiben zum anderen temporale Vorkommen von Zuständen, die durch ihre innere Struktur verbunden sind. Solche Eigenschaften können bisher von keiner der existierenden Logiken auf Graphen vergleichbar darstellt werden. Erstens führen wir Graphen mit Änderungshistorie ein, indem wir jedes Graphelement mit einem Zeitstempel seiner Erzeugung und, wenn nötig, seiner Löschung versehen. Zweitens definieren wir eine Logik auf Graphen, indem wir den Temporaloperator Until in die wohl-etablierte Logik der verschachtelten Graphbedingungen integrieren. Drittens beweisen wir, dass unsere Logik gleich ausdrucksmächtig ist, wie die Logik der verschachtelten Graphbedingungen, indem wir eine passende Reduktionsoperation definieren. Zuletzt erlaubt uns die Implementierung dieser Reduktionsoperation die werkzeukbasierte Analyse von metrisch-temporallogischen Eigenschaften für Zustandssequenzen zu führen.

Graph repair, restoring consistency of a graph, plays a prominent role in several areas of computer science and beyond: For example, in model-driven engineering, the abstract syntax of models is usually encoded using graphs. Flexible edit operations temporarily create inconsistent graphs not representing a valid model, thus requiring graph repair. Similarly, in graph databases—managing the storage and manipulation of graph data—updates may cause that a given database does not satisfy some integrity constraints, requiring also graph repair. We present a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of least-changing repairs. In our context, we formalize consistency by so-called graph conditions being equivalent to first-order logic on graphs. We present two kind of repair algorithms: State-based repair restores consistency independent of the graph update history, whereas deltabased (or incremental) repair takes this history explicitly into account. Technically, our algorithms rely on an existing model generation algorithm for graph conditions implemented in AutoGraph. Moreover, the delta-based approach uses the new concept of satisfaction (ST) trees for encoding if and how a graph satisfies a graph condition. We then demonstrate how to manipulate these STs incrementally with respect to a graph update.

Cyber-physical systems often encompass complex concurrent behavior with timing constraints and probabilistic failures on demand. The analysis whether such systems with probabilistic timed behavior adhere to a given specification is essential. When the states of the system can be represented by graphs, the rule-based formalism of Probabilistic Timed Graph Transformation Systems (PTGTSs) can be used to suitably capture structure dynamics as well as probabilistic and timed behavior of the system. The model checking support for PTGTSs w.r.t. properties specified using Probabilistic Timed Computation Tree Logic (PTCTL) has been already presented. Moreover, for timed graph-based runtime monitoring, Metric Temporal Graph Logic (MTGL) has been developed for stating metric temporal properties on identified subgraphs and their structural changes over time. In this paper, we (a) extend MTGL to the Probabilistic Metric Temporal Graph Logic (PMTGL) by allowing for the specification of probabilistic properties, (b) adapt our MTGL satisfaction checking approach to PTGTSs, and (c) combine the approaches for PTCTL model checking and MTGL satisfaction checking to obtain a Bounded Model Checking (BMC) approach for PMTGL. In our evaluation, we apply an implementation of our BMC approach in AutoGraph to a running example.

This book is Open Access under a CC BY licence. This book constitutes the proceedings of the 22nd International Conference on Fundamental Approaches to Software Engineering, FASE 2019, which took place in Prague, Czech Republic in April 2019, held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019.The 24 papers presented in this volume were carefully reviewed and selected from 94 submissions. The papers are organized in topical sections named: software verification; model-driven development and model transformation; software evolution and requirements engineering; specification, design, and implementation of particular classes of systems; and software testing.

This book constitutes the refereed proceedings of the 16th International Conference on Graph Transformation, ICGT 2023, held as Part of STAF 2023 in Leicester, UK, in July 2023. The 14 full papers and 2 short papers presented in this book were carefully reviewed and selected from 29 submissions. The conference focuses on describing new unpublished contributions in the theory and applications of graph transformation as well as tool presentation papers that demonstrate main new features and functionalities of graph-based tools.

This book constitutes the refereed proceedings of the 13th International Conference on Graph Transformation, ICGT 2020, in Bergen, Norway, in June 2020.* The 16 research papers and 4 tool paper presented in this book were carefully reviewed and selected from 40 submissions. One invited paper is also included. The papers deal with the following topics: theoretical advances; application domains; and tool presentations. *The conference was held virtually due to the COVID-19 pandemic.

The analysis of behavioral models is of high importance for cyber-physical systems, as the systems often encompass complex behavior based on e.g. concurrent components with mutual exclusion or probabilistic failures on demand. The rule-based formalism of probabilistic timed graph transformation systems is a suitable choice when the models representing states of the system can be understood as graphs and timed and probabilistic behavior is important. However, model checking PTGTSs is limited to systems with rather small state spaces. We present an approach for the analysis of large scale systems modeled as probabilistic timed graph transformation systems by systematically decomposing their state spaces into manageable fragments. To obtain qualitative and quantitative analysis results for a large scale system, we verify that results obtained for its fragments serve as overapproximations for the corresponding results of the large scale system. Hence, our approach allows for the detection of violations of qualitative and quantitative safety properties for the large scale system under analysis. We consider a running example in which we model shuttles driving on tracks of a large scale topology and for which we verify that shuttles never collide and are unlikely to execute emergency brakes. In our evaluation, we apply an implementation of our approach to the running example.