Datasets are essential for training and evaluating machine learning models. However, they are also the root cause of many undesirable model behaviors, such as biased predictions. To address this issue, the machine learning community is proposing as a best practice the adoption of common guidelines for describing datasets. However, these guidelines are based on natural language descriptions of the dataset, hampering the automatic computation and analysis of such descriptions. To overcome this situation, we present DescribeML, a language engineering tool to precisely describe machine learning datasets in terms of their composition, provenance, and social concerns in a structured format. The tool is implemented as a Visual Studio Code extension.

Recent public regulatory initiatives and relevant voices in the ML community have identified the need to document datasets according to several dimensions to ensure the fairness and trustworthiness of machine learning systems. In this sense, the data-sharing practices in the scientific field have been quickly evolving in the last years, with more and more research works publishing technical documentation together with the data for replicability purposes. However, this documentation is written in natural language, and its structure, content focus, and composition vary, making them challenging to analyze.

Abstract In recent years, we have seen many performance fiascos in the deployment of new systems, such as the US health insurance web. This paper describes the functionality and architecture, as well as success stories, of a tool that helps address these types of issues. The tool allows assessing software designs regarding quality, in particular performance and reliability. Starting from a UML design with quality annotations, the tool applies model-transformation techniques to yield analyzable models. Such models are then leveraged by the tool to compute quality metrics. Finally, quality results, over the design, are presented to the engineer, in terms of the problem domain. Hence, the tool is an asset for the software engineer to evaluate system quality through software designs. While leveraging the Eclipse platform, the tool uses UML and the MARTE, DAM and DICE profiles for the system design and the quality modeling.

Abstract In the Internet-of-Things (IoT) vision, everyday objects evolve into cyber-physical systems. The massive use and deployment of these systems has given place to the Industry 4.0 or Industrial IoT (IIoT). Due to its scalability requirements, IIoT architectures are typically distributed and asynchronous. In this scenario, one of the most widely used paradigms is publish/subscribe, where messages are sent and received based on a set of categories or topics. However, these architectures face interoperability challenges. Consistency in message categories and structure is the key to avoid potential losses of information. Ensuring this consistency requires complex data processing logic both on the publisher and the subscriber sides. In this paper, we present our proposal relying on AsyncAPI to automate the design and implementation of these asynchronous architectures using model-driven techniques for the generation of (part of) message-driven infrastructures. Our proposal offers two different ways of designing the architectures: either graphically, by modeling and annotating the messages that are sent among the different IoT devices, or textually, by implementing an editor compliant with the AsyncAPI specification. We have evaluated our proposal by conducting a set of experiments with 25 subjects with different expertise and background. The experiments show that one-third of the subjects were able to design and implement a working architecture in less than an hour without previous knowledge of our proposal, and an additional one-third estimated that they would only need less than two hours in total.

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 AI-augmented 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 auto-mate 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.

In this Internet of Things (IoT) era, our everyday objects have evolved into the so-called cyber-physical systems (CPS). The use and deployment of CPS has especially penetrated the industry, giving rise to the Industry 4.0 or Industrial IoT (IIoT). Typically, architectures in IIoT environments are distributed and asynchronous, communication being guided by events such as the publication of (and corresponding subscription to) messages.

The growing need to store and manipulate large volumes of data has led to the blossoming of various families of data storage solutions. Software modelers can benefit from this growing diversity to improve critical parts of their applications, using a combination of different databases to store the data based on access, availability, and performance requirements. However, while the mapping of conceptual schemas to relational databases is a well-studied field of research, there are few works that target the role of conceptual modeling in a multiple and diverse data storage settings. This is particularly true when dealing with the mapping of constraints in the conceptual schema. In this paper we present the UMLto[No]SQL approach that maps conceptual schemas expressed in UML/OCL into a set of logical schemas (either relational or NoSQL ones) to be used to store the application data according to the data partition envisaged by the designer. Our mapping covers as well the database queries required to implement and check the model's constraints. UMLto[No]SQL takes care of integrating the different data storages, and provides a modeling layer that enables a transparent manipulation of the data using conceptual level information.

UML sequence diagrams are used to graphically describe the message interactions between the objects participating in a certain scenario. Combined fragments extend the basic functionality of UML sequence diagrams with control structures, such as sequences, alternatives, iterations, or parallels. In this paper, we present a UML profile to annotate sequence diagrams with combined fragments to model timed Web services with distributed resources under the publish/subscribe paradigm. This profile is exploited to automatically obtain a representation of the system based on Colored Petri nets using a novel model-to-model (M2M) transformation. This M2M transformation has been specified using QVT and has been integrated in a new add-on extending a state-of-the-art UML modeling tool. Generated Petri nets can be immediately used in well-known Petri net software, such as CPN Tools, to analyze the system behavior. Hence, our model-to-model transformation tool allows for simulating the system and finding design errors in early stages of system development, which enables us to fix them at these early phases and thus potentially saving development costs.

Efficient processing of very large models is a key requirement for the adoption of Model-Driven Engineering (MDE) in some industrial contexts. One of the central operations in MDE is rule-based model transformation (MT). It is used to specify manipulation operations over structured data coming in the form of model graphs. However, being based on computationally expensive operations like subgraph isomorphism, MT tools are facing issues on both memory occupancy and execution time while dealing with the increasing model size and complexity. One way to overcome these issues is to exploit the wide availability of distributed clusters in the Cloud for the distributed execution of MT. In this paper, we propose an approach to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We show how the execution semantics of ATL can be aligned with the MapReduce computation model. We describe the extensions to the ATL transformation engine to enable distribution, and we experimentally demonstrate the scalability of this solution in a reverse-engineering scenario.

Despite the increasing interest in the Document Engineering community, a formal definition of document workflow is still to come. Often, the term refers to an abstract process consisting in a set of tasks to contribute to some document contents, and some techniques are being developed to support parts of these tasks rather than how to generate the process itself. In most proposals, these tasks are implicit in the business processes running in an organization, lacking an explicit document workflow model that could be analysed and enacted as a coherent unit. In this paper, we propose a document-centric approach to document workflow generation. We have extended the feature-based document meta-model of the Document Product Lines approach with an organiza-tional metamodel. For a given configuration of the feature model, we assign tasks to different members of the organization to con-tribute to the document contents. Moreover, the relationships between features define an ordering of the tasks, which may be refined to produce a specification of the document workflow model automatically. The generation of customized software manuals is used to illustrate the proposal.

Variable Data Printing solutions provide means to generate documents whose content varies according to some criteria. Since the early Mail Merge-like applications that generated letters with destination data taken from databases, different languages and frameworks have been developed with increasing levels of sophistication. Current tools allow the generation of highly customized documents that are variable not only in content, but also in layout. However, most frameworks are technology-oriented, and their use requires high skills in implementation-related tools (XML, XPATH, and others), which do not include support for domain-related tasks like identification of document content variability.

This paper presents our baseline oriented modeling (BOM) approach. BOM is a framework that automatically generates software applications as PRISMA architectural models using model transformations and software product line techniques. We follow the model-driven architecture initiative building domain models which are automatically transformed into platform independent models, and then compiled to an executable application (i.e. platform specific models). In order to illustrate BOM, we focus on a specific domain: the diagnostic expert systems.