Gergely Varró

Designing the automatic transformation of visual languages

The design process of complex systems requires a precise checking of the functional and dependability attributes of the target design. The growing complexity of systems necessitates the use of formal methods, as the exhaustiveness of checks performed by the traditional simulation and testing is insufficient.For this reason, the mathematical models of various formal verification tools are automatically derived from UML-diagrams of the model by mathematical transformations guaranteeing a complete consistency between the target design and the models of verification and validation tools.In the current paper, a general framework for an automated model transformation system is presented. The method starts from a uniform visual description and a formal proof concept of the particular transformations by integrating the powerful computational paradigm of graph transformation, planner algorithms of artificial intelligence, and various concepts of computer engineering.

Benchmarking for graph transformation

Model transformation (MT) is a key technology in the model-driven development approach of software engineering that provides automated means to capture the evolution of models and mappings between modeling languages. The pattern and rule-based paradigm of graph transformation is considered a very popular approach for specifying such model transformations. While the expressiveness of different MT specification techniques is frequently compared on well-known transformation problems (e.g. UML-to-XMI, or UML-to-EJB mappings), no such benchmarks exist currently for comparing the performance of different model transformation tools. In the paper, we propose a systematic method for quantitative benchmarking in order to assess the performance of graph transformation tools. Typical features of the graph transformation paradigm and various optimization strategies exploited in different toots are identified and categorized. Moreover, the performance of several popular graph transformation tools is measured and compared on a well-known distributed mutual exclusion problem.

Adaptive Graph Pattern Matching for Model Transformations using Model-sensitive Search Plans

The current paper makes two contributions for the graph pattern matching problem of model transformation tools. First, model-sensitive search plan generation is proposed for pattern traversal (as an extension to traditional multiplicity and type considerations of existing tools) by estimating the expected performance of search plans on typical instance models that are available at transformation design time. Then, an adaptive approach for graph pattern matching is presented, where the optimal search plan can be selected from previously generated search plans at run-time based on statistical data collected from the current instance model under transformation.

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.

Generic Search Plans for Matching Advanced Graph Patterns

In the current paper, we present search plans which can guide pattern matching for advanced graph patterns with edge identities, containment constraints, type variables, negative application conditions, attribute conditions, and injectivity constraints. Based on a generic search graph representation, all search plan operations (e.g. checking the existence of an edge, or extending a matching candidate by navigating along an edge) are uniformly represented as special predicates with heuristically assigned costs. Finally, an executable search plan is defined as an appropriate ordering of these predicates. As a main consequence, attribute, injectivity, and negative application conditions can be checked early (but not unnecessarily early) in the pattern matching process to cut off infeasible matching candidates at the right time.

Graph Transformation with Incremental Updates

We propose an efficient implementation technique for graph transformation systems based on incremental updates. The essence of the technique is to keep track of all possible matchings of graph transformation rules in database tables, and update these tables incrementally to exploit the fact that rules typically perform only local modifications to models.

Implementing a Graph Transformation Engine in Relational Databases

We present a novel approach to implement a graph transformation engine based on standard relational database management systems (RDBMSs). The essence of the approach is to create database views for each rule and to handle pattern matching by inner join operations while handling negative application conditions by left outer join operations. Furthermore, the model manipulation prescribed by the application of a graph transformation rule is also implemented using elementary data manipulation statements (such as insert, delete). As a result, we obtain a robust and fast transformation engine especially suitable for (1) extending modeling tools with an underlying RDBMS repository and (2) embedding model transformations into large distributed applications where models are frequently persisted in a relational database and transaction handling is required to handle large models consistently.

Efficient model synchronization with precedence triple graph grammars

Triple Graph Grammars (TGGs) are a rule-based technique with a formal background for specifying bidirectional and incremental model transformation. In practical scenarios, unidirectional rules for incremental forward and backward transformation are automatically derived from the TGG rules in the specification, and the overall transformation process is governed by a control algorithm. Current incremental implementations either have a runtime complexity that depends on the size of related models and not on the number of changes and their affected elements, or do not pursue formalization to give reliable predictions regarding the expected results. In this paper, a novel incremental model synchronization algorithm for TGGs is introduced, which employs a static analysis of TGG specifications to efficiently determine the range of influence of model changes, while retaining all formal properties.

Incremental Graph Pattern Matching: Data Structures and Initial Experiments

Despite the large variety of existing graph transformation tools, the implementation of their pattern matching engine typically follows the same principle. First a matching occurrence of the left-hand side of the graph transformation rule is searched by some graph pattern matching algorithm. Then potential negative application conditions are checked that might eliminate the previous occurrence. However, when a new transformation step is started, all the information on previous matchings is lost, and the complex graph pattern matching phase is restarted from scratch each time. In the paper, we present the foundational data structures and initial experiments for an incremental graph pattern matching engine which keeps track of existing matchings in an incremental way to reduce the execution time of graph pattern matching.