Model Driven Engineering

The definition and construction of complex computer-based systems require not just software engineering knowledge, but also many other domainspecific techniques to ensure many system's functional and non-functional properties. Hence, there is a trend to move away from programming languages to models on which one can reason: model-driven engineering. Yet, this remains a complex task: one need to master many techniques. In this paper, we claim that MDE is incomplete: it is "just" an implementation framework to support advanced model-based techniques, verification of systems non-functional properties, code generation, etc. There is a conceptual gap to fill to know "what" to do with models. We propose to switch from MDE to VDE: Verification-Driven Engineering, so that the user knows how to model a system to analyze it. We sum up existing techniques and their relevant application domains.

In this paper, we propose an approach to translate the SysML language to VHDL-AMS code. This approach is first step to the generation of the VHDL-AMS code from the structural diagrams SysML. In this step, we address the block definition diagram and the internal block Diagram. The translation uses Model Driven Engineer (MDE) methods as the transformation of model to another model (M2M) with ATL Atlas Transformation Language and the code generation from models (M2T) using Xpand. We provide the translation rules between the two elements. Implementation and methodology are illustrated on microsystem case study: the Smart surface system.

With the emergence of Cyber-Physical Systems (CPS), the increasing complexity in development and operation demands for an efficient engineering process. In the recent years DevOps promotes closer continuous integration of system development and its operational deployment perspectives. In this context, the use of Artificial Intelligence (AI) is beneficial to improve the system design and integration activities, however, it is still limited despite its high potential. AIDOaRT is a 3 years long H2020-ECSEL European project involving 32 organizations, grouped in clusters from 7 different countries, focusing on AIaugmented automation supporting modelling, coding, testing, monitoring and continuous development of Cyber-Physical Systems (CPS). The project proposes to apply Model-Driven Engineering (MDE) principles and techniques to provide a framework offering proper AI-enhanced methods and related tooling for building trustable CPSs. The framework is intended to work within the DevOps practices combining software development and information technology (IT) operations. In this regard, the project points at enabling AI for IT operations (AIOps) to automate decision making process and complete system development tasks. This paper presents an overview of the project with the aim to discuss context, objectives and the proposed approach.

Business Intelligence or Decision Support System (DSS) is IT for decision-makers and business leaders. It describes the means, the tools and the methods that make it possible to collect, consolidate, model and restore the data, material or immaterial, of a company to offer a decision aid and to allow a decision-maker to have an overview of the activity being treated. Given the large volume, variety, and data velocity we entered the era of Big Data. And since most of today's BI tools are designed to handle structured data. In our research project, we aim to consolidate a BI system for Big Data. In continuous efforts, this paper is a progress report of our first contribution that aims to apply the techniques of model engineering to propose a universal approach to deal with Big Data is to help decision-makers make decisions.

Model driven engineering community proposes the concepts and the tools to design more easily software applications. Complementary to the elements proposed, the use of a functional approach becomes more and more important in the field of complex software. More precisely, current works use the genericity of functional programming languages to describe more easily and formally metamodels and model transformations. This article proposes to show the benefits of such an approach with a set of Domain Specific Languages (DSLs) to design more quickly model transformations. The article describes a process to build DSLs that makes it possible the integration of textual models at the input or the output of a transformation, and the extraction or the add of information from/to a model.

Testing software in air traffic control systems costs much more than building them. Software engineers strive to find methodological and process-level solutions to balance costs and to better distribute verification efforts among all development phases. Modeldriven approaches could provide a solution. // s3pie.indd 42 3/29/13 5:40 PM m ay/J u n E 2 0 13 | I E E E S o f t wa r E 43

The workshop OCLApps 2006 was organized as a part of MoDELS/UML Conference in Genova, Italy. It continues the series of five OCL (Object Constraint Language) workshops held at previous UML/MoDELS conferences between 2000 - 2005. Similar to its predecessors, the workshop addressed both people from academia and industry. The advent of the MDA (Model Driven Architecture) vision and the rapid acceptance of MDE (Model Driven Engineering) approaches emphasize new application domains (like Semantic Web or Domain Specific Languages) and call for new OCL functionalities. In this context, the OCLApps 2006 Workshop, was conceived as a forum enabling researchers and industry experts to present and debate how the OCL could support these new requirements.

Software development process has a several challenges for development teams, challenges such as to understand business or client requirements and to provide global solutions covering all the organizational enviroment, keeping down the costs and adding value to the organizations.

With the addition of software in the majority of the organizational process and the necessity for to obtain finished products in less time, the development requirements began to change, for this reason phased development methodology has become obsolete.   

In order to adapt to the rapidly evolving industry, have appeared "SCRUM" which is a methodology for agile application development and "Test Driven Development" (TDD) which is an iterative object-oriented software design practice, which is included into "Extreme Programming” methodology (XP).

In Ecuador there is a large set of companies involved in software development that are using Java platform and JEE architecture for business core construction (BackEnd) into enterprise applications also use JavaScript Framewors implementation in order to optimize the speed of response in client-side presentation layer ( FrontEnd).

This article discusses the use of Test Driven Development (TDD) in conjunction with SCRUM for development of business-oriented Web application that uses AngunlasJS in FrondEnd and Java JEE in BackEnd

As software evolution researchers, we are well aware of two facts, formulated even as laws back in 1976 by Belady and Lehman: First, successful software systems are subjected to continuous change -in fact, the competetive advantage of a software system is more and more determined by its flexibility to undergo required changes. Second, each change erodes the structure of the design, making the software harder and harder to change. But how can we work in a world where both laws are true? How can we ever strike a balance between the need to keep our system evolvable, while at same time being forced to modify that system under tight time-to-market constraints? In this presentation we first of all explore the practical as well as the research implications of this "software evolution paradox". We then use the findings to reflect on our ongoing software evolution research, in particular in the area of aspect mining. We will present our approach based on fan-in analysis as well as the FINT Eclipse plugin supporting it. We apply fan-in analysis to a number of open source case studies, and critically analyze which of the concerns identified are suitable for an aspect-oriented refactoring. We use these results in order to reflect on the relevance of aspects and aspect mining for making life under the software evolution paradox a little easier.

RÉSUMÉ. Aujourd'hui, la transformation de modèles est unanimement reconnue comme étant au cœur des approches de l'ingénierie dirigée par les modèles (MDE, Model Driven Engineering). Malgré la multitude de langages de transformations de modèles issus des mondes universitaire et industriel, la transformation est souvent réalisée manuellement. Dans ce papier, nous proposons dans une première partie une extension à l'architecture du

Context: Managing Non-Functional Requirements (NFRs) in software projects is challenging, and projects that adopt Model-Driven Development (MDD) are no exception. Although several methods and techniques have been proposed to face this challenge, there is still little evidence on how NFRs are handled in MDD by practitioners. Knowing more about the state of the practice may help researchers to steer their research and practitioners to improve their daily work. Objective: In this paper, we present our findings from an interview-based survey conducted with practitioners working in 18 different companies from 6 European countries. From a practitioner's point of view, the paper shows what barriers and benefits the management of NFRs as part of the MDD process can bring to companies, how NFRs are supported by MDD approaches, and which strategies are followed when (some) types of NFRs are not supported by MDD approaches. Results: Our study shows that practitioners perceive MDD adoption as a complex process with little to no tool support for NFRs, reporting productivity and maintainability as the types of NFRs expected to be supported when MDD is adopted. But in general, companies adapt MDD to deal with NFRs. When NFRs are not supported, the generated code is sometimes changed manually, thus compromising the maintainability of the software developed. However, the interviewed practitioners claim that the benefits of using MDD outweight the extra effort required by these manual adaptations. Conclusion: Overall, the results indicate that it is important for practitioners to handle NFRs in MDD, but further research is necessary in order to lower the barrier for supporting a broad spectrum of NFRs with MDD. Still, much conceptual and tool implementation work seems to be necessary to lower the barrier of integrating the broad spectrum of NFRs in practice.