Abstract

Abstract

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 However, those models may not exist or may be outdated and so, the test case generation must resort to other artifacts.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.