The Epsilon Pattern Language

We present the Epsilon Pattern Language (EPL), a textual language that supports expressing and detecting patterns on models conforming to arbitrary metamodels and captured using diverse modelling technologies. EPL provides out-of-the-box integration with existing languages that target a wide range of related model management activities (such as model validation, model-to-model and model-to-text transformation), thus enabling code reuse and seamless runtime interoperability across complex Model-Driven Engineering workflows. We discuss the syntax and semantics of EPL, its supporting development tools, and demonstrate how instances of patterns detected using EPL can be consumed and further processed by other model management programs

Stress-Testing Remote Model Querying APIs for Relational and Graph-Based Stores

Recent research in scalable model-driven engineering now allows very large models to be stored and queried. Due to their size, rather than transferring such models over the network in their entirety, it is typically more efficient to access them remotely using networked services (e.g. model repositories, model indexes). Little attention has been paid so far to the nature of these services, and whether they remain responsive with an increasing number of concurrent clients. This paper extends a previous empirical study on the impact of certain key decisions on the scalability of concurrent model queries on two domains, using an Eclipse Connected Data Objects model repository, four configurations of the Hawk model index and a Neo4j-based configuration of the NeoEMF model store. The study evaluates the impact of the network protocol, the API design, the caching layer, the query language and the type of database, and analyses the reasons for their varying levels of performance. The design of the API was shown to make a bigger difference compared to the network protocol (HTTP/TCP) used. Where available, the query-specific indexed and derived attributes in Hawk outperformed the comprehensive generic caching in CDO. Finally, the results illustrate the still ongoing evolution of graph databases: two tools using different versions of the same backend had very different performance, with one slower than CDO and the other faster than it

Learn-ciam: a model-driven approach for the development of collaborative learning tools

This paper introduces Learn-CIAM, a new model-based methodological approach for the design of flows and for the semi-automatic generation of tools in order to support collaborative learning tasks. The main objective of this work is to help professors by establishing a series of steps for the specification of their learning courses and the obtaining of collaborative tools to support certain learning activities (in particular, for in-group editing, searching and modeling). This paper presents a complete methodological framework, how it is supported conceptually and technologically, and an application example. So to guarantee the validity of the proposal, we also present some validation processes with potential designers and users from different profiles such as Education and Computer Science. The results seem to demonstrate a positive reception and acceptance, concluding that its application would facilitate the design of learning courses and the generation of collaborative learning tools for professionals of both profiles

Model-driven engineering of an openCypher engine: using graph queries to compile graph queries

Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. Many challenging applications with near real-time requirements - such as financial fraud detection, on-the-fly model validation and root cause analysis - can be formalised as graph problems and tackled with graph databases efficiently. However, as no standard graph query language has yet emerged, users are subjected to the possibility of vendor lock-in. The openCypher group aims to define an open specification for a declarative graph query language. However, creating an openCypher-compatible query engine requires significant research and engineering efforts. Meanwhile, model-driven language workbenches support the creation of domain-specific languages by providing high-level tools to create parsers, editors and compilers. In this paper, we present an approach to build a compiler and optimizer for openCypher using model-driven technologies, which allows developers to define declarative optimization rules

Formalising openCypher Graph Queries in Relational Algebra

Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. However, due to the novelty of such systems, no standard data model or query language has yet emerged. Consequently, migrating datasets or applications even between related technologies often requires a large amount of manual work or ad-hoc solutions, thus subjecting the users to the possibility of vendor lock-in. To avoid this threat, vendors are working on supporting existing standard languages (e.g. SQL) or creating standardised languages. In this paper, we present a formal specification for openCypher, a high-level declarative graph query language with an ongoing standardisation effort. We introduce relational graph algebra, which extends relational operators by adapting graph-specific operators and define a mapping from core openCypher constructs to this algebra. We propose an algorithm that allows systematic compilation of openCypher queries.Comment: ADBIS conference (21st European Conference on Advances in Databases and Information Systems) The final publication is available at Springer via https://doi.org/10.1007/978-3-319-66917-5_1

Parallel model validation with epsilon

Traditional model management programs, such as transformations, often perform poorly when dealing with very large models. Although many such programs are inherently parallelisable, the execution engines of popular model management languages were not designed for concurrency. We propose a scalable data and rule-parallel solution for an established and feature-rich model validation language (EVL). We highlight the challenges encountered with retro-fitting concurrency support and our solutions to these challenges. We evaluate the correctness of our implementation through rigorous automated tests. Our results show up to linear performance improvements with more threads and larger models, with significantly faster execution compared to interpreted OCL

A Novel Approach to Mutation Operator Design for MDE Languages

Due to the increasing reliance on the software of systems, such as enterprise systems, a wide array of approaches has been found to facilitate the development of software for such systems. The growth of system complexity, however, has provoked concerns about the quality of the software. One approach that copes with complexity is model driven engineering that uses models containing only essential domain concepts, in order to represent complex systems. With some level of automation, models are then maintained by programs that are prone to error, as they are man-made. In order to find errors in programs, software engineers use mutation testing to build strong test inputs by introducing faults into the tested software using mutation operators. They then study if the introduced faults are detected by the test inputs. There have been few attempts to design mutation operators for model driven languages, which have common metamodeling language models, compared with traditional programming languages. This thesis presents an effective language-agnostic approach for mutation operator design for the rapidly emerging model driven engineering languages by considering metamodeling languages. The approach produces generic operators that can be instantiated to generate concrete ones for a given language model, which can be used to mutate program models that conform to the language model. The approach and generic operators are evaluated using empirical mutation analysis experiments over programs written in the ATL and EOL languages. The results show that the generic operators generated from the approach are instantiatable over ATL and EOL metamodels and have produced low proportions of invalid and equivalent mutants that can impact negatively on any mutation testing process. Also, the generic operators have produced useful mutants such as live and not trivially detected kinds of mutants

SEMI-AUTOMATED TEST MODEL GENERATION

Appropriate test models that can satisfy complex constraints are required for testing model management programs in order to build confidence in their correctness. Models have inherently complex structures and are often required to satisfy non-trivial constraints which makes them time consuming, labour intensive and error prone to construct manually. Automated capabilities are therefore required, however, existing fully-automated model generation tools cannot generate models that satisfy arbitrarily complex constraints. This thesis addresses this problem by proposing a semi-automated approach towards the generation of such models. A new framework named Epsilon Model Generator (EMG) that implements this approach is presented. The framework supports the development of model generators that can produce random and reproducible test models that satisfy complex constraints

Optimisation of Model Management Programs Using Automated Program Rewriting

Over the last few years, the use of Model Driven Engineering (MDE) in industrial applications has been increasing rapidly. For the use of MDE-based applications at a larger scale, they should scale well in terms of the size of the model, execution time, and memory consumption. Adapting the use of MDE for extensive software landscapes, where the underlying models grow large in size poses various scalability challenges. When model management tools run on pay-as-you-go cloud-based resources, inefficiency and limited scalability incur substantial costs. Hence, there is vested interest from vendors of cloud-based MDE solutions for efficient and scalable model management tools. There are specific high-level languages to develop model management programs tailored for the specific tasks they target. This work aims to improve the performance of certain types of model management programs through static analysis. An approach is proposed for optimising model management tasks, particularly model validation, model-to-model transformation and model comparison over large-scale models. The proposed approach leverages static analysis and automated program rewriting techniques to optimise model management programs over large-scale EMF-based models. This optimisation approach aims to bring efficiency in terms of execution time and memory footprint so that developers can still express model management programs in high-level language and execute these programs efficiently. The program is automatically rewritten to an optimised version (where possible). The optimised program is semantically equivalent to the original program but faster and more efficient to execute. The experiments of this study have shown a significant performance gain in execution time and memory footprint