Nicholas Drivalos Matragkas

A research roadmap towards achieving scalability in model driven engineering

As Model-Driven Engineering (MDE) is increasingly applied to larger and more complex systems, the current generation of modelling and model management technologies are being pushed to their limits in terms of capacity and efficiency. Additional research and development is imperative in order to enable MDE to remain relevant with industrial practice and to continue delivering its widely recognised productivity, quality, and maintainability benefits. Achieving scalability in modelling and MDE involves being able to construct large models and domain-specific languages in a systematic manner, enabling teams of modellers to construct and refine large models in a collaborative manner, advancing the state of the art in model querying and transformations tools so that they can cope with large models (of the scale of millions of model elements), and providing an infrastructure for efficient storage, indexing and retrieval of large models. This paper attempts to provide a research roadmap for these aspects of scalability in MDE and outline directions for work in this emerging research area.

Formal verification and validation of embedded systems: the UML-based MADES approach

Formal verification and validation activities from the early development phases can foster system consistency, correctness, and integrity, but they are often hard to carry out as most designers do not have the necessary background. To address this difficulty, a possible approach is to allow engineers to continue using familiar notations and tools, while verification and validation are performed on demand, automatically, and transparently. In this paper we describe how the problem of making formal verification and validation tasks more designer-friendly is tackled by the MADES approach. Our solution is based on a tool chain that is built atop mature, popular, and widespread technologies. The paper focuses on the verification and closed-loop simulation (validation) aspects of the approach and shows how it can be applied to significant embedded software systems.

A feature model for model-to-text transformation languages

Model-to-text (M2T) transformation is an important model management operation, as it is used to implement code and documentation generation; model serialisation (enabling model interchange); and model visualisation and exploration. Despite the creation of the MOF Model-To-Text Transformation Language (MOFM2T) in 2008, many very different M2T languages exist today. Because there is little interoperability between M2T languages and rewriting an existing M2T transformation in a new language is costly, developers face a difficult choice when selecting a M2T language. In this paper, we use domain analysis to identify a preliminary feature model for M2T languages. We demonstrate the appropriateness of the feature model by describing two different M2T languages, and discuss potential applications for a tool-supported and model-driven approach to describing the features of M2T languages.

Model-Based Hardware Generation and Programming - The MADES Approach

This paper gives an overview of the model-based hardware generation and programming approach proposed within the MADES project. MADES aims to develop a model-driven development process for safety-critical, real-time embedded systems. MADES defines a systems modelling language based on subsets of MARTE and SysML that allows iterative refinement from high-level specification down to final implementation. The MADES project specifically focusses on three unique features which differentiate it from existing model-driven development frameworks. First, model transformations in the Epsilon modelling framework are used to move between system models and provide traceability. Second, the Zot verification tool is employed to allow early and frequent verification of the system being developed. Third, Compile-Time Virtualisation is used to automatically retarget architecturally-neutral software for execution on complex embedded architectures. This paper concentrates on MADESs approach to the specification of hardware and the way in which software is refactored by Compile-Time Virtualisation.

Type Inference in Flexible Model-Driven Engineering

In Model-Driven Engineering (MDE), models conform to metamodels. In flexible modelling, engineers construct example models with free-form drawing tools; these examples may later need to conform to a metamodel. Flexible modelling can lead to errors: drawn elements that should represent the same domain concept could instantiate different types; other drawn elements could be left untyped. We propose a novel type inference approach to calculating types from example models, based on the Classification and Regression Trees (CART) algorithm. We describe the approach and evaluate it on a number of randomly generated models, considering the accuracy and precision of the resultant classifications. Experimental results suggest that on average 80% of element types are correctly identified. In addition, the results reveal a correlation between the accuracy and the ratio of known-to-unknown types in a model.

Models of OSS project meta-information: a dataset of three forges

The process of selecting open-source software (OSS) for adoption is not straightforward as it involves exploring various sources of information to determine the quality, maturity, activity, and user support of each project. In the context of the OSSMETER project, we have developed a forge-agnostic metamodel that captures the meta-information common to all OSS projects. We specialise this metamodel for popular OSS forges in order to capture forge-specific meta-information. In this paper we present a dataset conforming to these metamodels for over 500,000 OSS projects hosted on three popular OSS forges: Eclipse, SourceForge, and GitHub. The dataset enables different kinds of automatic analysis and supports objective comparisons of cross-forge OSS alternatives with respect to a users needs and quality requirements.

Analysing the 'biodiversity' of open source ecosystems: the GitHub case

In nature the diversity of species and genes in ecological communities affects the functioning of these communities. Biologists have found out that more diverse communities appear to be more productive than less diverse communities. Moreover such communities appear to be more stable in the face of perturbations. In this paper, we draw the analogy between ecological communities and Open Source Software (OSS) ecosystems, and we investigate the diversity and structure of OSS communities. To address this question we use the MSR 2014 challenge dataset, which includes data from the top-10 software projects for the top programming languages on GitHub. Our findings show that OSS communities on GitHub consist of 3 types of users (core developers, active users, passive users). Moreover, we show that the percentage of core developers and active users does not change as the project grows and that the majority of members of large projects are passive users.

Model Driven Grant Proposal Engineering

We demonstrate the application of Model Driven Engineering techniques to support the development of research grant proposals. In particular, we report on using model-to-text transformation and model validation to enhance productivity and consistency in research proposal writing, and present unanticipated opportunities that were revealed after establishing an MDE infrastructure. We discuss the types of models and the technologies used, reflect on our experiences, and assess the productivity benefits of our MDE solution through automated analysis of data extracted from the version control repository of a successful grant proposal; our evaluation indicates that the use of MDE techniques improved productivity by at least 58%.

Constraint programming for type inference in flexible model-driven engineering

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance. HighlightsIn Flexible MDE type omissions for nodes are common.A type inference approach for untyped nodes is proposed.The approach is based on constraint programming techniques.

Scalability in Model Driven Engineering: BigMDE'13 workshop summary

As Model Driven Engineering (MDE) is increasingly applied to larger and more complex systems, the current generation of modelling and model management technologies are being pushed to their limits in terms of capacity and efficiency. As such, additional research and development is imperative in order to enable MDE to remain relevant with industrial practice and to continue delivering its widely-recognised productivity, quality, and maintainability benefits. The need to reflect upon scalability in MDE led us to organise the 1st BigMDE international workshop co-located with the STAF (Software Technologies - Applications and Foundations) conferences, which was held at the Budapest University of Technology and Economics, Budapest, Hungary, during June 17-20, 2013.