Characteristics, potentials, and limitations of open-source Simulink projects for empirical research

Simulink is an example of a successful application of the paradigm of model-based development into industrial practice. Numerous companies create and maintain Simulink projects for modeling software-intensive embedded systems, aiming at early validation and automated code generation. However, Simulink projects are not as easily available as code-based ones, which profit from large publicly accessible open-source repositories, thus curbing empirical research. In this paper, we investigate a set of 1734 freely available Simulink models from 194 projects and analyze their suitability for empirical research. We analyze the projects considering (1) their development context, (2) their complexity in terms of size and organization within projects, and (3) their evolution over time. Our results show that there are both limitations and potentials for empirical research. On the one hand, some application domains dominate the development context, and there is a large number of models that can be considered toy examples of limited practical relevance. These often stem from an academic context, consist of only a few Simulink blocks, and are no longer (or have never been) under active development or maintenance. On the other hand, we found that a subset of the analyzed models is of considerable size and complexity. There are models comprising several thousands of blocks, some of them highly modularized by hierarchically organized Simulink subsystems. Likewise, some of the models expose an active maintenance span of several years, which indicates that they are used as primary development artifacts throughout a project’s lifecycle. According to a discussion of our results with a domain expert, many models can be considered mature enough for quality analysis purposes, and they expose characteristics that can be considered representative for industry-scale models. Thus, we are confident that a subset of the models is suitable for empirical research. More generally, using a publicly available model corpus or a dedicated subset enables researchers to replicate findings, publish subsequent studies, and use them for validation purposes. We publish our dataset for the sake of replicating our results and fostering future empirical research.

Early Validation of Functional Requirements and Transition to Source Code with Event-B

The cost of fixing software requirements errors after deployment is so high that it is vital to come up with ways to find and fix requirements errors early in the life-cycle of a project. The work in this paper advocates for the use of formal methods as an alternative approach to guarantee the correctness of the software from requirements to code. We present a formal-methods based approach for the early validation of functional requirements. Our approach relies on formal methods techniques such as program refinement, correctness-by-construction (CbyC), and automated code generation. We present two case studies that showcase our approach; for the case studies, we discuss design decisions, flaws encountered, and lessons learned.

Recent Progress in Automated Code Generation from GUI Images Using Machine Learning Techniques

The manual transformation of a user interface design into code is a costly and time-consuming process. A solution can be the automation of the generation of code based on sketches or GUI design images. Recently, Machine Learning approaches have shown promising results in detecting GUI elements for such automation. Thus, to provide an overview of existing approaches, we performed a systematic mapping study. As a result, we identified and compared 20 approaches, that demonstrate good performance results being considered useful. These results can be used by researchers and practitioners in order to improve the efficiency of the GUI design process as well as continue to evolve and improve approaches for its support.

An ontology-based framework for automated code generation of Web AR applications

In this paper, we propose an ontology-based framework for automated Web AR application code generation leveraging semantic descriptions of application structure and behavior in synergy with domain knowledge and annotations of building information model (BIM). Three case studies are presented: smart home energy consumption monitoring application, robotics testbed companion and virtual music mixing interface. The presented approach significantly reduces the time needed for Web AR application development.