Inductive Invariant Checking With Partial Negative Application Conditions

Graph transformation systems are a powerful formal model to capture model transformations or systems with infinite state space, among others. However, this expressive power comes at the cost of rather limited automated analysis capabilities. The general case of unbounded many initial graphs or infinite state spaces is only supported by approaches with rather limited scalability or expressiveness. In this report we improve an existing approach for the automated verification of inductive invariants for graph transformation systems. By employing partial negative application conditions to represent and check many alternative conditions in a more compact manner, we can check examples with rules and constraints of substantially higher complexity. We also substantially extend the expressive power by supporting more complex negative application conditions and provide higher accuracy by employing advanced implication checks. The improvements are evaluated and compared with another applicable tool by considering three case studies.

While offering significant expressive power, graph transformation systems often come with rather limited capabilities for automated analysis, particularly if systems with many possible initial graphs and large or infinite state spaces are concerned. One approach that tries to overcome these limitations is inductive invariant checking. However, the verification of inductive invariants often requires extensive knowledge about the system in question and faces the approach-inherent challenges of locality and lack of context. To address that, this report discusses k-inductive invariant checking for graph transformation systems as a generalization of inductive invariants. The additional context acquired by taking multiple (k) steps into account is the key difference to inductive invariant checking and is often enough to establish the desired invariants without requiring the iterative development of additional properties. To analyze possibly infinite systems in a finite fashion, we introduce a symbolic encoding for transformation traces using a restricted form of nested application conditions. As its central contribution, this report then presents a formal approach and algorithm to verify graph constraints as k-inductive invariants. We prove the approach's correctness and demonstrate its applicability by means of several examples evaluated with a prototypical implementation of our algorithm.

This book constitutes the proceedings of the 8th International Conference on Graph Transformations, ICGT 2015, held in L'Aquila, Italy, in July 2015. The 15 full papers presented together with 3 short papers and 1 keynote were carefully reviewed and selected from 27 submissions. The papers are organized in topical sections on foundations; applications: technical papers, and tool presentations.

This book constitutes the refereed proceedings of the 10th International Conference on Graph Transformation, ICGT 2017, held as part of STAF 2017, in Marburg, Germany, in July 2017. The 14 papers presented were carefully reviewed and selected from 23 submissions. The papers cover a wide range of topics including theoretical approaches to graph transformation and their verification, model-driven engineering, chemical reactions as well as various applications. They are organized in the following topical sections: foundations; graph language and parsing; analysis and verification; and model transformation and tools.

The correctness of model transformations is a crucial element for model-driven engineering of high quality software. In particular, behavior preservation is the most important correctness property avoiding the introduction of semantic errors during the model-driven engineering process. Behavior preservation verification techniques either show that specific properties are preserved, or more generally and complex, they show some kind of behavioral equivalence or refinement between source and target model of the transformation. Both kinds of behavior preservation verification goals have been presented with automatic tool support for the instance level, i.e. for a given source and target model specified by the model transformation. However, up until now there is no automatic verification approach available at the transformation level, i.e. for all source and target models specified by the model transformation. In this report, we extend our results presented in [27] and outline a new sophisticated approach for the automatic verification of behavior preservation captured by bisimulation resp. simulation for model transformations specified by triple graph grammars and semantic definitions given by graph transformation rules. In particular, we show that the behavior preservation problem can be reduced to invariant checking for graph transformation and that the resulting checking problem can be addressed by our own invariant checker even for a complex example where a sequence chart is transformed into communicating automata. We further discuss today's limitations of invariant checking for graph transformation and motivate further lines of future work in this direction.

This book constitutes thoroughly revised and selected papers from the 6th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2018, held in Funchal, Madeira, Portugal, in January 2018. The 22 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 101 submissions. They contribute to the development of highly relevant research trends in model-driven engineering and software development such as innovative methods for MDD-based development and testing of web-based applications and user interfaces, support for development of Domain-Specific Languages (DSLs), MDD-based application development on multiprocessor platforms, advances in MDD tooling, formal semantics and behaviour modelling, and MDD-based product-line engineering.

Graph queries have lately gained increased interest due to application areas such as social networks, biological networks, or model queries. For the relational database case the relational algebra and generalized discrimination networks have been studied to find appropriate decompositions into subqueries and ordering of these subqueries for query evaluation or incremental updates of query results. For graph database queries however there is no formal underpinning yet that allows us to find such suitable operationalizations. Consequently, we suggest a simple operational concept for the decomposition of arbitrary complex queries into simpler subqueries and the ordering of these subqueries in form of generalized discrimination networks for graph queries inspired by the relational case. The approach employs graph transformation rules for the nodes of the network and thus we can employ the underlying theory. We further show that the proposed generalized discrimination networks have the same expressive power as nested graph conditions.

Every year, the Hasso Plattner Institute (HPI) invites guests from industry and academia to a collaborative scientific workshop on the topic “Operating the Cloud”. Our goal is to provide a forum for the exchange of knowledge and experience between industry and academia. Hence, HPI’s Future SOC Lab is the adequate environment to host this event which is also supported by BITKOM. On the occasion of this workshop we called for submissions of research papers and practitioner’s reports. ”Operating the Cloud” aims to be a platform for productive discussions of innovative ideas, visions, and upcoming technologies in the field of cloud operation and administration. In this workshop proceedings the results of the third HPI cloud symposium ”Operating the Cloud” 2015 are published. We thank the authors for exciting presentations and insights into their current work and research. Moreover, we look forward to more interesting submissions for the upcoming symposium in 2016.