Detecting and Refactoring Operational Smells within the Domain Name System

The Domain Name System (DNS) is one of the most important components of the Internet infrastructure. DNS relies on a delegation-based architecture, where resolution of names to their IP addresses requires resolving the names of the servers responsible for those names. The recursive structures of the inter dependencies that exist between name servers associated with each zone are called dependency graphs. System administrators' operational decisions have far reaching effects on the DNSs qualities. They need to be soundly made to create a balance between the availability, security and resilience of the system. We utilize dependency graphs to identify, detect and catalogue operational bad smells. Our method deals with smells on a high-level of abstraction using a consistent taxonomy and reusable vocabulary, defined by a DNS Operational Model. The method will be used to build a diagnostic advisory tool that will detect configuration changes that might decrease the robustness or security posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244

04101 Abstracts Collection -- Language Engineering for Model-Driven Software Development

From 29.02. to 05.03.04, the Dagstuhl Seminar 04101 ``Language Engineering for Model-Driven Software Development\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A Compositional Approach to Structuring and Refinement of Typed Graph Grammars

Abstract Based on a categorical semantics that has been developed for typed graph grammars we uses colimits (pushouts) to model composition and (reverse) graph grammar morphisms to describe refinements of typed graph grammars. Composition of graph grammars w.r.t. common subgrammars is shown to be compatible with the semantics, i.e. the semantics of the composed grammar is obtained as the composition of the semantics of the component grammars. Moreover, the structure of a composed grammar is preserved during a refinement step in the sense that compatible refinements of the components induce a refinement of the composition. The concepts and results are illustrated by an example

Graph Rewriting for Graph Neural Networks

Given graphs as input, Graph Neural Networks (GNNs) support the inference of nodes, edges, attributes, or graph properties. Graph Rewriting investigates the rule-based manipulation of graphs to model complex graph transformations. We propose that, therefore, (i) graph rewriting subsumes GNNs and could serve as formal model to study and compare them, and (ii) the representation of GNNs as graph rewrite systems can help to design and analyse GNNs, their architectures and algorithms. Hence we propose Graph Rewriting Neural Networks (GReNN) as both novel semantic foundation and engineering discipline for GNNs. We develop a case study reminiscent of a Message Passing Neural Network realised as a Groove graph rewriting model and explore its incremental operation in response to dynamic updates.Comment: Originally submitted to ICGT 2023, part of STAF Conference

Rule-Level Verification of Business Process Transformations using CSP

Business Process Reengineering is one of the most widely adopted techniques to improve the efficiency of organisations. Transforming process models, we intend to change their semantics in certain predefined ways, making them more flexible, more restrictive, etc. To understand and control the semantic consequences of change we use CSP to capture the behaviour of processes before and after the transformation. Formalising process transformations by graph transformation rules, we are interested in verifying semantic properties of these transformations at the level of rules, so that every application of a rule has a known semantic effect. It turns out that we can do so if the mapping of activity diagrams models into the semantic domain CSP is compositional, i.e., compatible with the embedding of processes into larger contexts

View-based Modelling and State-Space Generation for Graph Transformation Systems

Modelling complex systems by graph transformation, we face scalability challenges both in our ability to create and understand these models and in the ability of tools to analyse them. To address these problems we propose to model graph transformation systems in views which can be understood and analysed separately. In particular, we show that transition systems can be generated separately for different views which, when synchronised using a CSP-like operator, yield a system that is bisimilar to the original global system

From Graph Transformations to Differential Equations

In a variety of disciplines models are used to predict, measure or explain quantitative properties. Examples include the concentration of a chemical substance produced within a given period, the growth of the size of a population of individuals, the time taken to recover from a communication breakdown in a network, etc. The models such properties arise from are often discrete and structural in nature. Adding information on the time and/or probability of any actions performed, quantitative models can be derived. In the first example above, commonly referred to as kinetic analysis of chemical reactions, a system of differential equations describing the evolution of concentrations is extracted from specifications of individual chemical reactions augmented with reaction rates. Recently, this construction has inspired approaches based on stochastic process specification techniques aiming to extract a continuous, quantitative model of a system from a discrete, structural one. This paper describes a methodology for such an extraction based on stochastic graph transformations. The approach is based on a variant of the construction of critical pairs and has been implemented using the AGG tool and validated for a simple reaction of unimolecular nucleophilic substitution (SN1)