Using ATL transformation services in the MDEForge collaborative modeling platform

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-42064-6_5In the last years, the increasing complexity of Model-Driven Engineering (MDE) tools and techniques has led to higher demands in terms of computation, interoperability, and configuration management. Harnessing the softwareas- a-service (SaaS) paradigm and shifting applications from local, mono-core implementations to cloud-based architectures is key to enhance scalability and flexibility. To this end, we propose MDEForge: an extensible, collaborative modeling platform that provides remote model management facilities and prevents the user from focussing on time-consuming, and less creative procedures. This demo paper illustrates the extensibility of MDEForge by integrating ATL services for the remote execution, automated testing, and static analysis of ATL transformations. The usefulness of their employment under the SaaS paradigm is demonstrated with a case-study showing a wide range of new application possibilities.Work supported by the Spanish MINECO (TIN2014-52129-R), the Madrid Region (S2013/ICE-3006), and the EU commission (#611125

Qualifying chains of transformation with coverage based evaluation criteria

Abstract. In Model-Driven Engineering (MDE) the development of complex and large transformations can benefit from the reuse of smaller ones that can be composed according to user requirements. Composing transformations is a complex problem: typically smaller transformations are discovered and selected by developers from different and heterogeneous sources. Then the identified transformations are chained by means of manual and error-prone composition processes. Based on our approach, when we propose one or more transformation chains to the user, it is difficult for him to choose one path instead of another without considering the semantic properties of a transformation. In this paper when multiple chains are proposed to the user, according to his requirements, we propose an approach to classify these suitable chains with respect to the coverage of the metamodels involved in the transformation. Based on coverage value, we are able to qualify the transformation chains with an evaluation criteria which gives as an indication of how much information a transformation chain covers over another

Supporting Early-Safety Analysis of IoT Systems by Exploiting Testing Techniques

IoT systems complexity and susceptibility to failures pose significant challenges in ensuring their reliable operation Failures can be internally generated or caused by external factors impacting both the systems correctness and its surrounding environment To investigate these complexities various modeling approaches have been proposed to raise the level of abstraction facilitating automation and analysis FailureLogic Analysis FLA is a technique that helps predict potential failure scenarios by defining how a components failure logic behaves and spreads throughout the system However manually specifying FLA rules can be arduous and errorprone leading to incomplete or inaccurate specifications In this paper we propose adopting testing methodologies to improve the completeness and correctness of these rules How failures may propagate within an IoT system can be observed by systematically injecting failures while running test cases to collect evidence useful to add complete and refine FLA rule

Automated reuse of model transformations through typing requirements models

Model transformations are key elements of model-driven engineering, where they are used to automate the manipulation of models. However, they are typed with respect to concrete source and target meta-models, making their reuse for other (even similar) meta-models challenging. To improve this situation, we propose capturing the typing requirements for reusing a transformation with other meta-models by the notion of a typing requirements model (TRM). A TRM describes the prerequisites that amodel transformation imposes on the source and targetmeta-models to obtain a correct typing. The key observation is that any meta-model pair that satisfies the TRM is a valid reuse context for the transformation at hand. A TRM is made of two domain requirement models (DRMs) describing the requirements for the source and target meta-models, and a compatibility model expressing dependencies between them. We define a notion of refinement between DRMs and see meta-models as a special case of DRM. We provide a catalogue of valid refinements and describe how to automatically extract a TRM from an ATL transformation. The approach is supported by our tool TOTEM. We report on two experiments-based on transformations developed by third parties and meta-model mutation techniques-validating the correctness and completeness of our TRM extraction procedure and confirming the power of TRMs to encode variability and support flexible reuseWork partially funded by the R&D programme of the Madrid Region (project FORTE, S2018/TCS4314), the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00), the Spanish MINECO(project RECOM, TIN2015-73968-JIN, AEI/FEDER/UE), a Ramón y Cajal 2017 grant, and the European Union Horizon 2020 research and innovation programme through the Polyglot and Hybrid Persistence Architectures for Big Data Analytics (TYPHON) project (#780251

OSSMETER: Automated measurement and analysis of open source software

International audienceDeciding whether an open source software (OSS) meets the requiredstandards for adoption in terms of quality, maturity, activity of development anduser support is not a straightforward process. It involves analysing various sourcesof information, including the project’s source code repositories, communicationchannels, and bug tracking systems. OSSMETER extends state-of-the-art techniquesin the field of automated analysis and measurement of open-source software(OSS), and develops a platform that supports decision makers in the processof discovering, comparing, assessing and monitoring the health, quality, impactand activity of opensource software. To achieve this, OSSMETER computestrustworthy quality indicators by performing advanced analysis and integrationof information from diverse sources including the project metadata, source coderepositories, communication channels and bug tracking systems of OSS projects

Mining correlations of ATL model transformation and metamodel metrics

Model transformations are considered to be the "heart" and "soul" of Model Driven Engineering, and as a such, advanced techniques and tools are needed for supporting the development, quality assurance, maintenance, and evolution of model transformations. Even though model transformation developers are gaining the availability of powerful languages and tools for developing, and testing model transformations, very few techniques are available to support the understanding of transformation characteristics. In this paper, we propose a process to analyze model transformations with the aim of identifying to what extent their characteristics depend on the corresponding input and target metamodels. The process relies on a number of transformation and metamodel metrics that are calculated and properly correlated. The paper discusses the application of the approach on a corpus consisting of more than 90 ATL transformations and 70 corresponding metamodels

Collaborative Repositories in Model-Driven Engineering

Model- driven engineering (MDE) is increasingly used across industries to abstract designs and viewpoints. Development productivity improves owing to faster change cycles. However, many current MDE tools are suitable for drawing but won\u27t scale up. Roundtrip for maintenance, tool interoperability, and team collaboration are far from industry needs. But there\u27s a light on the horizon with a new generation of MDE tools. In this issue\u27s column, Alfonso Pierantonio and his team provide an overview of recent MDE technologies. I look forward to hearing from both readers and prospective column authors about this column and the technologies you want to know more about