Gábor Bergmann

Incremental evaluation of model queries over EMF models

Model-driven development tools built on industry standard platforms, such as the Eclipse Modeling Framework (EMF), heavily utilize model queries in 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 runtime performance and end user experience. However, due to their complexity, these queries can be time consuming to implement and optimize on a case-by-case basis. Consequently, there is a need for a model query framework that combines an easy-touse and concise declarative query formalism with high runtime performance. In this paper, we propose a declarative EMF model query framework using the graph pattern formalism as the query specification language. These graph patterns describe the arrangement and properties of model elements that correspond to, e.g. a well-formedness constraint, or an application context of a model transformation rule. For improved runtime performance, we employ incremental pattern matching techniques: matches of patterns are stored and incrementally maintained upon model manipulation. As a result, query operations can be executed instantly, independently of the complexity of the constraint and the size of the model. We demonstrate our approach in an industrial (AUTOSAR) model validation context and compare it against other solutions.

EMF-IncQuery

As model management platforms are gaining industrial attention, the importance of automated model querying techniques is also increasing. Several important engineering tasks supported by model-based tools - such as well-formedness constraint validation or model transformations - rely on efficiently evaluating model queries. If the models change rapidly or frequently, it is beneficial to provide live and incrementally evaluated queries that automatically propagate model changes to keep query results consistent.The current paper reports on the of EMF-IncQuery framework focusing on new features of its integrated development environment (such as query validation and visualization) and its support for integrating queries to existing applications (e.g. by auto-generated data bindings) built on top of the industry standard Eclipse Modeling Framework (EMF). Our approach is illustrated on a case study integrating well-formedness constraints to the Papyrus UML tool by live model queries of EMF-IncQuery with negligible additional manual programming effort.

Incremental pattern matching in the viatra model transformation system

Incremental pattern matching is a key challenge for many tool integration, model synchronization and (discrete-event) model simulation tasks. An incremental pattern matching engine explicitly stores existing matches, while these matches are maintained incrementally with respect to the changes of the underlying model. In the current paper, we present an adaptation of RETE networks [6] in order to provide incremental support for the transformation language of the VIATRA2 framework. We evaluate the performance of the incremental engine on a benchmark problem assessing the speedup of incremental processing in the case of as-long-as-possible type of rule applications.

Change-driven model transformations

In this paper, we investigate change-driven model transformations, a novel class of transformations, which are directly triggered by complex model changes carried out by arbitrary transactions on the model (e.g. editing operation, transformation, etc). After a classification of relevant change scenarios, we identify challenges for change-driven transformations. As the main technical contribution of the current paper, we define an expressive, high-level language for specifying change-driven transformations as an extension of graph patterns and graph transformation rules. This language generalizes previous results on live model transformations by offering trigger events for arbitrarily complex model changes, and dedicated reactions for specific kinds of changes, making this way the concept of change to be a first-class citizen of the transformation language. We discuss how the underlying transformation engine needs to be adapted in order to use the same language uniformly for different change scenarios. The technicalities of our approach will be discussed on a (1) model synchronization case study with non-materialized target models and (2) a case study on detecting the violation of evolutionary (temporal) constraints in the security requirements engineering domain.

A graph query language for EMF models

While model queries are important components in modeldriven tool chains, they are still frequently implemented using traditional programming languages, despite the availability of model query languages due to performance and expressiveness issues. In the current paper, we propose EMF-INCQUERY as a novel, graph-based query language for EMF models by adapting the query language of the VIATRA2 model transformation framework to inherit its concise, declarative nature, but to properly tailor the new query language to the modeling specificities of EMF. The EMF-INCQUERY language includes (i) structural restrictions for queries imposed by EMF models, (ii) syntactic sugar and notational shorthand in queries, (iii) true semantic extensions which introduce new query features, and (iv) a constraint-based static type checking method to detect violations of EMF-specific type inference rules.

Live Model Transformations Driven by Incremental Pattern Matching

In the current paper, we introduce a live model transformation framework, which continuously maintains a transformation context such that model changes to source inputs can be readily identified, and their effects can be incrementally propagated. Our framework builds upon an incremental pattern matcher engine, which keeps track of matches of complex contextual constraints captured in the form of graph patterns. As a result, complex model changes can be treated as elementary change events. Reactions to the changes of match sets are specified by graph transformation rules with a novel transactional execution semantics incorporating both pseudo-parallel and serializable behaviour.

A Benchmark Evaluation of Incremental Pattern Matching in Graph Transformation

In graph transformation, the most cost-intensive phase of a transformation execution is pattern matching, where those subgraphs of a model graph are identified and matched which satisfy constraints prescribed by graph patterns. Incremental pattern matching aims to improve the efficiency of this critical step by storing the set of matches of a graph transformation rule and incrementally maintaining it as the model changes, thus eliminating the need of recalculating existing matches of a pattern. In this paper, we propose benchmark examples where incremental pattern matching is expected to have advantageous effect in the application domain of model simulation and model synchronization. Moreover, we compare the incremental graph pattern matching approach of Viatra2 with advanced non-incremental local-search based graph pattern matching approaches (as available in Viatra2 and GrGen).

Back-annotation of Simulation Traces with Change-Driven Model Transformations

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.

IncQuery-D: A Distributed Incremental Model Query Framework in the Cloud

Queries are the foundations of data intensive applications. In model-driven software engineering (MDE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, traditional tools exhibit scalability issues that decrease productivity and increase costs [17]. While scalability is a hot topic in the database community and recent NoSQL efforts have partially addressed many shortcomings, this happened at the cost of sacrificing the ad-hoc query capabilities of SQL. Unfortunately, this is a critical problem for MDE applications due to their inherent workload complexity. In this paper, we aim to address both the scalability and ad-hoc querying challenges by adapting incremental graph search techniques – known from the EMF-IncQuery framework – to a distributed cloud infrastructure. We propose a novel architecture for distributed and incremental queries, and conduct experiments to demonstrate that IncQuery-D, our prototype system, can scale up from a single workstation to a cluster that can handle very large models and complex incremental queries efficiently.

Translating OCL to Graph Patterns

Model-driven tools use model queries for many purposes, including validation of well-formedness rules and specification of derived features. The majority of declarative model query corpus available in industry appears to use the OCL language. Graph pattern based queries, however, would have a number of advantages due to their more abstract specification, such as performance improvements through advanced query evaluation techniques. As query performance can be a key issue with large models, evaluating graph patterns instead of OCL queries could be useful in practice.