Abstract
The dropout of university students has been a factor of concern for educational institutions, affecting various aspects such as the institution’s reputation and funding and rankings. For this reason, it is essential to identify which students are at risk. In this study, algorithms based on decision trees and random forests are proposed to solve these problems using real data from 331 students from the University of Trásos-Montes and Alto Douro. In this work with these learning algorithms together with the training strategies, we managed to obtain an 89% forecast of students who may abandon their studies based on the evaluations of both semesters related to the first year and personal data. © 2021, Springer Nature Switzerland AG.

Abstract

Abstract

Abstract
Automatic test case generation is usually based on models of the software under test. However, those models may not exist or may be outdated and so, the test case generation must resort to other artifacts. In a software maintenance context, test cases must adapt to software changes and should be improved continuously to test adequately the new versions of the software. Mutation testing is a fault-based testing technique that evaluates the quality of the tests by applying simple changes to the source code and checking afterwards if the tests are able to detects those changes. This paper presents a web testing approach in which test cases are generated from user execution traces as a way to deal with the absence of models. In addition, it applies mutation operators over those test cases to enrich the test suite. The mutation operators were designed so as to mimic possible real failures. The additional tests are analyzed, and those that generate different outcomes are kept because they exercise additional behavior of the web application under test. At the end, the overall approach is illustrated and validated in a case study.

Abstract
Software testing can promote software quality. However, this activity is often performed at the end of projects where failures are most difficult to correct. Combining requirements specification activities with test design at an early stage of the software development process can be beneficial. One way to do this is to use a more structured requirements specification language. This allow to reduce typical problems such as ambiguity, inconsistency, and incorrectness in requirements and may allow the automatic generation of (parts of) acceptance test cases reducing the test design effort. In this paper we discuss an approach that promotes the practice of requirements specification combined with testing specification. This is a model-based approach that promotes the alignment between requirements and tests, namely, test cases and also low-level automated test scripts. To show the applicability of this approach, we integrate two complementary languages: (i) the ITLingo RSL (Requirements Specification Language) that is specially designed to support both requirements and tests rigorously and consistently specified; and (ii) the Robot language, which is a low-level keyword-based language for specifying test scripts. This approach includes model-to-model transformation processes, namely a transformation process from requirements (defined in RSL) into test cases (defined in RSL), and a second transformation process from test cases (in RSL) into test scripts (defined according the Robot framework). This approach was applied in a fictitious online store that illustrates the various phases of the proposal.

Abstract
This paper presents Alloy4Fun, a web application that enables online editing and sharing of Alloy models and instances (including dynamic ones developed with the Electrum extension), to be used mainly in an educational context. By introducing secret paragraphs and commands in the models, Alloy4Fun allows the distribution and automated assessment of simple specification challenges, a mechanism that enables students to learn the language at their own pace. Alloy4Fun stores all versions of shared and analyzed models, as well as derivation trees that depict how they evolved over time: this wealth of information can be mined by researchers or tutors to identify, for example, learning breakdowns in the class or typical mistakes made by Alloy users. Alloy4Fun has been used in formal methods graduate courses for two years and for the latest edition we present results regarding its adoption by the students, as well as preliminary insights regarding the most common bottlenecks when learning Alloy (and Electrum). © Springer Nature Switzerland AG 2020.