Many domain-specific languages, that try to bring feasible alternatives for existing solutions while simplifying programming work, have come up in recent years. Although, these little languages seem to be easy to use, there is an open issue whether they bring advantages in comparison to the application libraries, which are the most commonly used implementation approach. In this work, we present an experiment, which was carried out to compare such a domain-specific language with a comparable application library. The experiment was conducted with 36 programmers, who have answered a questionnaire on both implementation approaches. The questionnaire is more than 100 pages long. For a domain-specific language and the application library, the same problem domain has been used - construction of graphical user interfaces. In terms of a domain-specific language, XAML has been used and C# Forms for the application library. A cognitive dimension framework has been used for a comparison between XAML and C# Forms.

Many tools have been constructed using different formal methods to process various parts of a language specification (e.g. scanner generators, parser generators and compiler generators). The automatic generation of a complete compiler was the primary goal of such systems, but researchers recognised the possibility that many other language-based tools could be generated from formal language specifications. Such tools can be generated automatically whenever they can be described by a generic fixed part that traverses the appropriate data structures generated by a specific variable part, which can be systematically derivable from the language specifications. The paper identifies generic and specific parts for various language-based tools. Several language-based tools are presented in the paper, which are automatically generated using an attribute grammar-based compiler generator called LISA. The generated tools that are described in the paper include editors, inspectors, debuggers and visualisers/animators. Because of their complexity of construction, special emphasis is given to visualisers/animators, and the unique contribution of our approach toward generating such tools.

This paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students’ performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students’ data help an analyst to discover patterns. The coordinated visualization of aggregated students’ performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students’ behavior at different degrees. The analysis of the impact of the student’s explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.

The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity $\mathcal{O}\left(1\right)$ were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor.

In the past, we have been looking for program comprehension tools that are able to interconnect operational and behavioral views, aiming at aiding the software analyst to relate problem and program domains in order to reach a full understanding of software systems. In this paper we are concerned with Program Comprehension issues applied to Domain Specific Languages (DSLs). We are now willing to understand how techniques and tools for the comprehension of traditional programming languages fit in the understanding of DSLs. Being the language tailored for the description of problems in a specific domain, we believe that specific visualizations (at a higher abstraction level, closer to the problem level) could and should be defined to enhance the comprehension of the descriptions in that particular domain. .