Model-driven performance evaluation for service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised for the empirical performance evaluation of model-driven developed service systems

Requirements traceability in model-driven development: Applying model and transformation conformance

The variety of design artifacts (models) produced in a model-driven design process results in an intricate relationship between requirements and the various models. This paper proposes a methodological framework that simplifies management of this relationship, which helps in assessing the quality of models, realizations and transformation specifications. Our framework is a basis for understanding requirements traceability in model-driven development, as well as for the design of tools that support requirements traceability in model-driven development processes. We propose a notion of conformance between application models which reduces the effort needed for assessment activities. We discuss how this notion of conformance can be integrated with model transformations

From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)

In this paper, we illustrate how to enhance an existing state-of-the-art modeling language and tool for the Internet of Things (IoT), called ThingML, to support machine learning on the modeling level. To this aim, we extend the Domain-Specific Language (DSL) of ThingML, as well as its code generation framework. Our DSL allows one to define things, which are in charge of carrying out data analytics. Further, our code generators can automatically produce the complete implementation in Java and Python. The generated Python code is responsible for data analytics and employs APIs of machine learning libraries, such as Keras, Tensorflow and Scikit Learn. Our prototype is available as open source software on Github.Comment: International Conference on Model Driven Engineering Languages and Systems (MODELS) 2020 Poster Companion (Extended Abstract

Integrating the AUTOSAR tool chain with Eclipse based model transformations

International audienceAUTOSAR is establishing itself as a prominent standard in automotive systems and is expected to significantly improve software architecture and the software development processes. However, the introduction of AUTOSAR also poses some challenges

Applying a model-based methodology to develop web-based systems of systems

Systems of Systems (SoS) are emerging applications composed by subsystems that interacts in a distributed and heterogeneous environment. Web-based technologies are a current trend to achieve SoS user interaction. Model Driven Web Engineering (MDWE) is the application of Model Driven Engineering (MDE) into the Web development domain. This paper presents a MDWE methodology to include Web-based interaction into SoS development. It's composed of ten models and seven model transformations and it's fully implemented in a support tool for its usage in practice. Quality aspects covered through the traceability from the requirements to the nal code are exposed. The feasibility of the approach is validated by its application into a real-world project. A preliminary analysis of potential benets (reduction of eort, time, cost; improve of quality; design vs code ratio, etc) is done by comparison to other project as an initial hypothesis for a future planned experimentation research.Ministerio de Economía, Industria y Competitividad TIN2013-46928-C3- 3-RMinisterio de Economía, Industria y Competitividad TIN2015-71938-RED

A modular metamodel and refactoring rules to achieve software product line interoperability.

Emergent application domains, such as cyber–physical systems, edge computing or industry 4.0. present a high variability in software and hardware infrastructures. However, no single variability modeling language supports all language extensions required by these application domains (i.e., attributes, group cardinalities, clonables, complex constraints). This limitation is an open challenge that should be tackled by the software engineering field, and specifically by the software product line (SPL) community. A possible solution could be to define a completely new language, but this has a high cost in terms of adoption time and development of new tools. A more viable alternative is the definition of refactoring and specialization rules that allow interoperability between existing variability languages. However, with this approach, these rules cannot be reused across languages because each language uses a different set of modeling concepts and a different concrete syntax. Our approach relies on a modular and extensible metamodel that defines a common abstract syntax for existing variability modeling extensions. We map existing feature modeling languages in the SPL community to our common abstract syntax. Using our abstract syntax, we define refactoring rules at the language construct level that help to achieve interoperability between variability modeling languages.Work supported by the projects MEDEA RTI2018-099213-B-I00, IRIS PID2021-122812OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/FEDER, UE), LEIA UMA18-FEDERIA-157, and DAEMON H2020-101017109. // Funding for open access: Universidad de Málaga / CBUA

UAV-CLOUD: A PLATFORM FOR UAV RESOURCES AND SERVICES ON THE CLOUD

UAVs - Unmanned Aerial Vehicles – have gained significant attention recently, due to the increasingly growing range of applications. However, developing collaborative UAV applications using traditional technologies in a tightly coupled design requires a great deal of development effort, time, and budget especially for heterogeneous UAVs. Moreover, monitoring and accessing UAV resources using traditional communication media suffer from several restrictions and limitations. This research aims to simplify the efforts, reduce the time, and lower the costs of developing collaborative applications for distributed heterogeneous UAVs. In addition, the research aims to provide ubiquitous UAV resources access. A platform is proposed for developing distributed UAVs. This platform provides services to simplify application development. In this approach, UAVs are integrated with the Cloud Computing paradigm to provide ubiquitous access to their resources and services. Due to the limited capabilities of UAVs, a lightweight architecture is adopted. UAV resources and services are modeled in a Resource Oriented Architecture which is a new flexible web service design pattern with loosely coupled interaction between services. Hence, they are accessed as Representational State Transfer RESTful services using HTTP. Moreover, the research proposes using a broker architecture to increase efficiency by separating responsibilities. Therefore, it separates the requester’s logic and functionalities from the provider’s. It also takes the responsibility for allocating the issued request to the available and suitable UAV(s). To test the proposed platform, I first developed the UAV resources as a payload subsystem then provided them with Internet connectivity. Then, resource identifiers and uniform interfaces were developed using the RESTful Application Programming Interfaces (APIs). I also developed the broker service along with a database containing the information of the registered UAVs and their resources. The platform system components were tested using a requester interface in a browser by issuing a request for a resource to the broker to find and request the service from a suitable UAV. The test was done for retrieving data from UAVs as well as requesting actions from them. The main contributions of this research are proposing the UAV-Cloud platform for simplifying the development of ubiquitous UAV applications and its vii perspectives, as well as a lightweight loosely coupled design for UAV resources. Another contribution is developing the broker architecture for separating responsibilities in this platform

Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform

Traditional industries like steelmaking, are in the spotlight for the need of improving processes towards net zero emissions. This article presents a case on a new business model to ease the adoption of Machine Learning (ML) to optimize industrial processes, applied to a blast furnace at a steel company. The focus of the paper is to illustrate the way a ML platform with a Low Code solution approach can give results in two months to optimize a production process at a steel mill. The methodology used in the case allows obtaining a data model to be validated in less time than conventional approaches. This work pretends to give more light to the use of industrial data and the way traditional industries can evolve towards the industry 4.0 paradigm. The adoption of the low code solution is based on lean startup methodology. The cycle to obtain valid results includes the involvement of people from the process as well as analytics experts. At the end it can be seen that the solution contribute to improve Operational Equipment Effectiveness (OEE) and lower energy consumption. Besides process operators became empowered with the predictions that give the platform.Instituto de Investigación en Informátic

Use of domain-specific language in test automation

The primary aim of this research project was to investigate techniques to replace the complicated process of testing embedded systems in automotive domain. The multi-component domain was composed of different hardware to be used in testing procedure which increased the level of difficulty in testing for an operator. As a result, an existing semi-automated testing procedure was replaced by more simpler and efficient framework (ViBATA). A key step taken in this scenario was the replacement of manual GUI interface with the scriptable one to enhance the automation. This was achieved by building a Domain-specific language which allowed test definition in the form of human readable scripts which could be stored for later use. A DSL is a scripting language defined for a particular domain with compact expressiveness. In this case the domain is testing embedded systems in general and automotive systems in particular. The final product was a test case specification document in the form of XML as an output of generated code from this DSL which will be input to ViBATA to make test specification component automated. In this research a comparative analysis of existing DSLs for alternative domains and investigation of their applicability to the presented domain was also performed. The technologies used in this project are Xtext to define the DSL grammar, Xtend to generate code in Java and Simple framework to generate output in XML. The stages involved in DSL development and how these stages were implemented is covered in this thesis. The developed DSL for this domain is tested for automotive and calculator systems in this thesis which proved that this is more general and flexible. The DSL is consistent, efficient and automated test specification component of testing framework in embedded systems