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.

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. Rule-based DSE 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.

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.