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The current release of VIATRA provides opensource 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. Software tools in systems engineeringModel-driven engineering (MDE) plays an important role in the design of critical embedded and cyber-physical systems in various application domains such as automotive, avionics or telecommunication. MDE tools aim to simultaneously improve quality and decrease costs by early validation by highlighting conceptual design flaws well before traditional testing phases in accordance with the correct-by-construction principle. Furthermore, they improve productivity of engineers by automatically synthesizing different design artifacts (source code, configuration tables, test cases, fault trees, etc.) necessitated by certification standards (like DO-178C [117], DO-330 [116] or ISO 26262[78]).Certain shares in the software tool market of systems engineering are dominated by very few industrial tools (e.g., MATLAB Simulink, Dymola, DOORS, MagicDraw) each of which typically provides advanced support for certain development stages (requirements engineering, simulation, allocation, test generation, etc). To protect their intellectual property rights, these tools are of closed nature, which implies huge tool integration costs for system integrators (such as airframers or car manufacturers). On the other hand, recent initiatives (such as PolarSys, OpenModelica) have started to promote open language standards and the systematic use of open-source software components in tools for critical systems to reduce licensing costs and risks of vendor lock-in.Certification standards of critical cyber-physical systems require that software tools used for developing such critical system are validated with the same scrutiny as the system under design by software tool qualification [87,116], especially, when no further human checking is carried out on the outputs of such tools. Software tool qualification distinguishes between design tools which, by definition, may 123

Design space exploration (DSE) aims to find optimal design candidates of a domain with respect to different objectives where design candidates are constrained by complex structural and numerical restrictions. 14,18] aims to find such candidates that are reachable from an initial model by applying a sequence of exploration rules. Solving a rule-based DSE problem is a difficult challenge due to the inherently dynamic nature of the problem.In the current paper, we propose to integrate multi-objective optimization techniques by using Non-dominated Sorting Genetic Algorithms (NSGA) to drive rule-based design space exploration. For this purpose, finite populations of the most promising design candidates are maintained wrt. different optimization criteria. In our context, individuals of a generation are defined as a sequence of rule applications leading from an initial model to a candidate model. Populations evolve by mutation and crossover operations which manipulate (change, extend or combine) rule execution sequences to yield new individuals.Our multi-objective optimization approach for rule-based DSE is domain independent and it is automated by tooling built on the Eclipse framework. The main added value is to seamlessly lift multi-objective optimization techniques to the exploration process preserving both domain independence and a high-level of abstraction. Design candidates will still be represented as models and the evolution of these models as rule execution sequences. Constraints are captured by model queries while objectives can be derived both from models or rule applications. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

Abstract-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.

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 and algebraic abstraction of transformation rules. Additionally, we apply different state encoding techniques to identify recurring states and reduce the number of visited states. Finally, we illustrate our approach on a cloud infrastructure configuration problem and provide detailed evaluation on both synthetic and real applications. This evaluation includes (i) the comparison of several exploration techniques, (ii) performance measurements on multiple state encoding techniques and (iii) comparing two implementation architectures of our design space exploration framework.

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Model-driven tools frequently rely on advanced technologies to support model queries, view maintenance, design rule validation, model transformations or design space exploration. Some of these features are initiated explicitly by domain engineers (batch execution) while others are executed automatically when certain trigger events are detected (live execution). Unfortunately, their integration into a complex industrial modeling environment is dicult due to hidden interference and unspecied interaction between dierent features. In this paper, we present a reactive, event-driven model transformation platform over EMF models, which captures tool features as model queries and transformations, and provides a systematic, well-founded integration between a variety of such tool features. Viatra 3 oers a family of internal DSLs (i.e. dedicated libraries) to specify advanced tool features built on top of existing languages like EMF-IncQuery and Xtend. Its main innovation is a source incremental execution scheme built on the reactive programming paradigm ssupported by an event-driven virtual machine.

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