Large-scale model-driven system engineering projects are carried out collaboratively. Engineering artefacts stored in model repositories are developed in either offline (checkout–modify–commit) or online (GoogleDoc-style) scenarios. Complex systems frequently integrate models and components developed by different teams, vendors and suppliers. Thus, confidentiality and integrity of design artefacts need to be protected in accordance with access control policies. We propose a secure collaborative modelling approach where fine-grained access control for models is strictly enforced by bidirectional model transformations. Collaborators obtain filtered local copies of the model containing only those model elements which they are allowed to read; write access control policies are checked on the server upon submitting model changes. We present a formal collaboration schema which provenly guarantees certain correctness constraints, and its adaption to online scenarios with on-the-fly change propagation and the integration into existing version control systems to support offline scenarios. The approach is illustrated, and its scalability is evaluated using a case study of the MONDO EU project.

The article “Enforcing fine-grained access control for secure collaborative modelling using bidirectional transformations”, written by Csaba Debreceni, Gábor Bergmann, István Ráth, Dániel Varró, was originally published electronically on the publisher’s internet portal (https://link.springer.com/journal/10270) on [11/21/2017 6:24:42 AM] without open access.

Streaming model transformations represent a novel class of transformations to manipulate models whose elements are continuously produced or modified in high volume and with rapid rate of change. Executing streaming transformations requires efficient techniques to recognize activated transformation rules over a live model and a potentially infinite stream of events. In this paper, we propose foundations of streaming model transformations by innovatively integrating incremental model query, complex event processing (CEP) and reactive (event-driven) transformation techniques. Complex event processing allows to identify relevant patterns and sequences of events over an event stream. Our approach enables event streams to include model change events which are automatically and continuously populated by incremental model queries. Furthermore, a reactive rule engine carries out transformations on identified complex event patterns. We provide an integrated domain-specific language with precise semantics for capturing complex event patterns and streaming transformations together with an execution engine, all of which is now part of the Viatra reactive transformation framework. We demonstrate the feasibility of our approach with two case studies: one in an advanced model engineering workflow; and one in the context of on-the-fly gesture recognition.

In model-driven development of safety-critical systems (like automotive, avionics or railways), well-formedness of models is repeatedly validated in order to detect design flaws as early as possible. In many industrial tools, validation rules are still often implemented by a large amount of imperative model traversal code which makes those rule implementations complicated and hard to maintain. Additionally, as models are rapidly increasing in size and complexity, efficient execution of validation rules is challenging for the currently available tools. Checking well-formedness constraints can be captured by declarative queries over graph models, while model update operations can be specified as model transformations. This paper presents a benchmark for systematically assessing the scalability of validating and revalidating well-formedness constraints over large graph models. The benchmark defines well-formedness validation scenarios in the railway domain: a metamodel, an instance model generator and a set of well-formedness constraints captured by queries, fault injection and repair operations (imitating the work of systems engineers by model transformations). The benchmark focuses on the performance of query evaluation, i.e. its execution time and memory consumption, with a particular emphasis on reevaluation. We demonstrate that the benchmark can be adopted to various technologies and query engines, including modeling tools; relational, graph and semantic databases. The Train Benchmark is available as an open-source project with continuous builds from https://github.com/FTSRG/trainbenchmark.

Large-scale model-driven engineering projects are carried out collaboratively. Enabling a high degree of concurrency is required to make the traditionally rigid development processes more agile. The increasing number of collaborators increases the probability of introducing conflicts which need to be resolved manually by the collaborators. In case of highly interdependent models, avoiding conflicts by the use of locks can save valuable time. However, traditional locking techniques such as fragment-based and object-based strategies may impose unnecessary restrictions on editing, which can decrease the efficiency of collaboration.In this paper, we propose a property-based locking approach that generalizes traditional locking techniques, and further allows more fine-grained locks in order to restrict modifications only when necessary. A lock is considered to be violated if a match appears or disappears for its associated graph pattern (formula), which captures the property of the model that the upcoming edit transaction can be freely executed. An initial evaluation has been carried out using a case study of the MONDO EU project.

Model-based systems engineering of critical cyber-physical systems necessitates effective collaboration between different stakeholders while still providing secure protection of intellectual properties of all involved parties. While engineering artifacts are frequently stored in version control repositories, secure access control is limited to file-level strategies in most existing frameworks where models are split into multiple fragments with all-or-nothing permissions, which becomes a scalability and usability bottleneck in case of complex industrial models.

The current release of VIATRA provides open-source tool support for an event-driven, reactive model transformation engine built on top of highly scalable incremental graph queries for models with millions of elements and advanced features such as rule-based design space exploration complex event processing or model obfuscation. However, the history of the VIATRA model transformation framework dates back to over 16 years. Starting as an early academic research prototype as part of the M.Sc project of the the first author it first evolved into a Prolog-based engine followed by a family of open-source projects which by now matured into a component integrated into various industrial and open-source tools and deployed over multiple technologies. This invited paper briefly overviews the evolution of the VIATRA/IncQuery family by highlighting key features and illustrating main transformation concepts along an open case study influenced by an industrial project.

Queries are the foundations of data intensive applications. In model-driven software engineering (MDSE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, most MDSE tools frequently exhibit scalability issues that decrease developer productivity and increase costs. As a result, choosing the right model representation and query evaluation approach is a significant challenge for tool engineers. In the current paper, we aim to provide a benchmarking framework for the systematic investigation of query evaluation performance. More specifically, we experimentally evaluate (existing and novel) query and instance model metrics to highlight which provide sufficient performance estimates for different MDSE scenarios in various model query tools. For that purpose, we also present a comparative benchmark, which is designed to differentiate model representation and graph query evaluation approaches according to their performance when using large models and complex queries.

Model-driven development tools built on industry standard platforms, such as the Eclipse Modeling Framework (EMF), heavily use model queries in various use cases, such as model transformation, well-formedness constraint validation and domain-specific model execution. As these queries are executed rather frequently in interactive modeling applications, they have a significant impact on the runtime performance of the tool, and also on the end user experience. However, due to their complexity, they can be time consuming to implement and optimize on a case-by-case basis. To address these shortcomings, we developed the EMF-INCQUERY framework for defining declarative queries over EMF models and executing them effectively using a caching mechanism. In the current paper, we demonstrate how our framework can be easily integrated with other EMF tools. We describe a case study in which EMF-INCQUERY is integrated into the open source Papyrus UML environment to provide on-the-fly validation of well-formedness criteria in UML models.

Design space exploration (DSE) aims at searching through various models representing different design candidates to support activities like configuration design of critical systems or automated maintenance of IT systems. In model-driven engineering, DSE is applied to find instance models that are (i) reachable from an initial model with a sequence of transformation rules and (ii) satisfy a set of structural and numerical constraints. Since exhaustive exploration of the design space is infeasible for large models, the traversal is often guided by hints, derived by system analysis, to prioritize the next states to traverse (selection criteria) and to avoid searching unpromising states (cut-off criteria). In this paper, we define an exploration approach where selection and cut-off criteria are defined using dependency analysis of transformation rules and an algebraic abstraction. The approach is evaluated against other exploration techniques and illustrated on a cloud infrastructure configuration problem.

Domain-specific modeling languages (DSML) proved to be an important asset in creating powerful design tools for domain experts. Although these tools are capable of preserving the syntax-correctness of models even during free-hand editing, they often lack the ability of maintaining model consistency for complex language-specific constraints. Hence, there is a need for a tool-level automatism to assist DSML users in resolving consistency violation problems. In this paper, we describe an approach for the automatic generation of quick fixes for DSMLs, taking a set of domain-specific constraints and model manipulation policies as input. The computation relies on statespace exploration techniques to find sequences of operations that decrease the number of inconsistencies. Our approach is illustrated using a BPMN case study, and it is evaluated by several experiments to show its feasibility and performance.

Model-driven analysis aims at detecting design flaws early in high-level design models by automatically deriving mathematical models. These analysis models are subsequently investigated by formal verification and validation (V&V) tools, which may retrieve traces violating a certain requirement. Back-annotation aims at mapping back the results of V&V tools to the design model in order to highlight the real source of the fault, to ease making necessary amendments. Here we propose a technique for the back-annotation of simulation traces based on change-driven model transformations. Simulation traces of analysis models will be persisted as a change model with high-level change commands representing macro steps of a trace. This trace is back-annotated to the design model using change-driven transformation rules, which bridge the conceptual differences between macro steps in the analysis and design traces. Our concepts will be demonstrated on the back-annotation problem for analyzing BPEL processes using a Petri net simulator.

In this paper, we present a general purpose discrete event simulation framework for domain-specific visual languages describing system behavior. In our framework, the dynamic semantics of the language is captured by a combination of graph transformation and abstract state machine rules as provided by the model transformation language of VIATRA2, which allows to capture complex model changes at each simulation step. For an efficient execution of the simulation, incremental graph pattern matching is used to avoid the re-computation of enabledness conditions of simulation rules by incrementally keeping track of rule contexts.