Practicing Domain-Specific Languages: From Code to Models

International audienceThis paper describes our experience in constructing a new Domain-Specific Language course at the graduate level whose objectives is to reconciliate concepts coming from Language Design as well as Modeling domains. We illustrate the course using the reactive systems application domain, which prevents us to fall back in a toy example pitfall. This paper describes the nine stages used to guide students through a journey starting at low-level C code to end with the usage of a language design workbench. This course was given as a graduate course available at Université Côte d'Azur (8 weeks, engineering-oriented) and École Normale Supérieure de Lyon (13 weeks, research-oriented)

Capacity Planning in Virtualised Environments Using Model Driven Engineering

Capacity planning is an important activity in computing for optimising resource usage while avoiding performance degradation. The demand for computing resources is triggered by application workloads running on virtual or physical machines. With today's technology, resource scalability can be achieved through server virtualisation, by having scalable virtual machines running on a physical server. However, these scalable virtual resources run on limited physical resources, especially in small to medium scale data centres. The management of virtual and physical resources impacts upon application performance and introduces a cost for all parties. There is a need to measure the virtual and physical resource requirements in facilitating cost-effective capacity planning. This research identifies three main management phases for a capacity planning process for a data centre implementing server virtualisation: capturing application workloads, managing virtual resources and managing physical resources. This research proposes an approach that leverages domain specific modelling and model transformation to estimate resource requirements based on predicted application workloads for certain time periods. Model-Driven Engineering (MDE) was utilised to automate the identified process. A transparent, automated and repeatable MDE process for generating predictions for resource usage from workload models and sets of Domain Specific Modelling Languages (DSMLs) that allow resource and workloads logs as well as predicted workloads to be precisely captured using models were designed, implemented and evaluated with case studies. The MDE process exploits model transformation, comparison and merging, is modularised so that it can be configured for different kinds of capacity planning applications and technical infrastructures

Incremental Model-to-Text Transformation

Model-driven engineering (MDE) promotes the use of abstractions to simplify the development of complex software systems. Through several model management tasks (e.g., model verification, re-factoring, model transformation), many software development tasks can be automated. For example, model-to-text transformations (M2T) are used to realize textual development artefacts (e.g., documentation, configuration scripts, code, etc.) from underlying source models. Despite the importance of M2T transformation, contemporary M2T languages lack support for developing transformations that scale. As MDE is applied to systems of increasing size and complexity, a lack of scalable M2T transformations and other model management tasks hinders industrial adoption. This is largely due to the fact that model management tools do not support efficient propagation of changes from models to other development artefacts. As such, the re-synchronisation of generated textual artefacts with underlying system models can take considerably large amount of time to execute due to redundant re-computations. This thesis investigates scalability in the context of M2T transformation, and proposes two novel techniques that enable efficient incremental change propagation from models to generated textual artefacts. In contrast to existing incremental M2T transformation technique, which relies on model differencing, our techniques employ fundamentally different approaches to incremental change propagation: they use a form of runtime analysis that identifies the impact of source model changes on generated textual artefacts. The structures produced by this runtime analysis, are used to perform efficient incremental transformations (scalable transformations). This claim is supported by the results of empirical evaluation which shows that the techniques proposed in this thesis can be used to attain an average reduction of 60% in transformation execution time compared to non-incremental (batch) transformation

Towards Automated Formal Analysis of Model Transformation Specifications

In Model-Driven Engineering, model transformation is a key model management operation, used to translate models between notations. Model transformation can be used for many engineering activities, for instance as a preliminary to merging models from different meta- models, or to generate codes from diagrammatic models. A mapping model needs to be developed (the transformation specification) to represent relations between concepts from the metamodels. The evaluation of the mapping model creates new challenges, for both conventional verification and validation, and also in guaranteeing that models generated by applying the transformation specification to source models still retain the intention of the initial transformation requirements. Most model transformation creates and evaluates a transformation specification in an ad-hoc manner. The specifications are usu- ally unstructured, and the quality of the transformations can only be assessed when the transformations are used. Analysis is not systematically applied even when the transformations are in use, so there is no way to determine whether the transformations are correct and consistent. This thesis addresses the problem of systematic creation and analysis of model transformation, via a facility for planning and designing model transformations which have conceptual-level properties that are tractable to formal analysis. We proposed a framework that provides steps to systematically build a model transformation specification, a visual notation for specifying model transformation and a template-based approach for producing a formal specification that is not just structure-equivalent but also amenable to formal analysis. The framework allows evaluation of syntactic and semantic correctness of generated models, metamodel coverage, and semantic correctness of the transformations themselves, with the help of snapshot analysis using patterns

Incremental Model-to-Text Transformation

Model-driven engineering (MDE) promotes the use of abstractions to simplify the development of complex software systems. Through several model management tasks (e.g., model verification, re-factoring, model transformation), many software development tasks can be automated. For example, model-to-text transformations (M2T) are used to realize textual development artefacts (e.g., documentation, configuration scripts, code, etc.) from underlying source models. Despite the importance of M2T transformation, contemporary M2T languages lack support for developing transformations that scale. As MDE is applied to systems of increasing size and complexity, a lack of scalable M2T transformations and other model management tasks hinders industrial adoption. This is largely due to the fact that model management tools do not support efficient propagation of changes from models to other development artefacts. As such, the re-synchronisation of generated textual artefacts with underlying system models can take considerably large amount of time to execute due to redundant re-computations. This thesis investigates scalability in the context of M2T transformation, and proposes two novel techniques that enable efficient incremental change propagation from models to generated textual artefacts. In contrast to existing incremental M2T transformation technique, which relies on model differencing, our techniques employ fundamentally different approaches to incremental change propagation: they use a form of runtime analysis that identifies the impact of source model changes on generated textual artefacts. The structures produced by this runtime analysis, are used to perform efficient incremental transformations (scalable transformations). This claim is supported by the results of empirical evaluation which shows that the techniques proposed in this thesis can be used to attain an average reduction of 60% in transformation execution time compared to non-incremental (batch) transformation