Abstract

Abstract
It is important to properly test developed software because this may contribute to fewer bugs going unreported in deployed software. Often, little attention is spent on the topic of software testing in curricula, yielding graduate students without adequate preparation to deal with the quality standards required by the industry. This problem could be tackled by introducing bite-sized software testing education capsules that allow teachers to introduce software testing to their students in a less time-consuming manner and with a hands-on component that will facilitate learning. In order to design appropriate software testing educational tools, it is necessary to consider both the software testing needs of the industry and the cognitive models of students. This work-in-progress paper proposes an experimental design to gain an understanding of the cognitive strategies used by students during test case design based on real-life cases. Ultimately, the results of the experiment will be used to develop educational support for teaching software testing.

Abstract

Abstract
Regression testing is a software engineering maintenance activity that involves re-executing test cases on a modified software system to check whether code changes introduce new faults. However, it can be time-consuming and resource-intensive, especially for large systems. Regression testing selection techniques can help address this issue by selecting a subset of test cases to run. The change-based technique selects a subset of test cases based on the modified software classes, reducing the test suite size. Thereby, it will cover a smaller number of classes, decreasing the efficiency of the test suite to reveal design flaws. From this perspective, code smells are known to identify poor design and threaten the quality of software systems. In this study, we propose an approach to combine code change and smell to select regression tests and present two new techniques: code smell based and code change and smell. Additionally, we developed the Regression Testing Selection Tool (RTST) to automate the selection process. We empirically evaluated the approach in Defects4J projects by comparing the new techniques' effectiveness with the change-based as a baseline. The results show that the change-based technique achieves the highest reduction rate in the test suite size but with less class coverage. On the other hand, test cases selected using code smells and changed classes combined can potentially find more bugs. The code smell-based technique provides a comparable class coverage to the code change and smell approach. Our findings highlight the benefits of incorporating code smells in regression testing selection and suggest opportunities for improving the efficiency and effectiveness of regression testing.

Abstract
In a world increasingly present online, people are leaving a digital footprint, with valuable information scattered on the Web, in an unstructured manner, beholden to the websites that keep it. While there are potential harms in being able to access this information readily, such as enabling corporate surveillance, there are also significant benefits when used, for example, in journalism or investigations into Human Trafficking. This paper presents an approach for retrieving domain-specific information present on the Web using Social Media platforms as a gateway to other content existing on any website. It begins by identifying relevant profiles, then collecting links shared in posts to webpages related to them, and lastly, extracting and indexing the information gathered. The tool developed based on this approach was tested for a case study in the domain of Human Trafficking, more specifically in sexual exploitation, showing promising results and potential to be applied in a real-world scenario.

Abstract
Research data management is essential for safeguarding and prospecting data generated in a scientific context. Specific issues arise regarding data in image format, as this data typology poses particular challenges and opportunities; however, not much attention has been given to data as images. We reviewed 109 articles from several research domains where images were used either as data or metadata to understand how researchers specifically deal with this data format, and what are your habits and behaviors. We use the Web of Science (WoS), considering its five main areas of research. We included in the initial corpus the most relevant articles by research domain, selecting the ten most cited articles in WoS, by year, between 2010 and 2021. The selected articles should be in English and in open access. The results found that images have been used in scientific works numerous times, but, unfortunately, few are those in which they are the central element of the study. Photography is the type of image most used in most domains. In terms of the instruments used, the Technology and Life Sciences and Biomedicine domains use the microscope more, while the Arts and Humanities and Physical Sciences domains use the camera more. We found that the images are mostly produced in the context of the project, rather than reused by third parties. As for their collection scenario, these are mostly produced/used in a laboratory context. The overwhelming majority of the images present in the articles are digital, and only a small part is analog. We verify that Arts and Humanities are more likely to perform qualitative types of analyses, while Life Sciences and Biomedicine overwhelmingly use quantitative analyses. As for the issues of sharing and depositing, Life Sciences and Biomedicine is the domain that stands out the most in the tasks of depositing and sharing images. It was found that the licenses of a project are intrinsically related to the motivations for sharing results with third parties. Description, a fundamental step in the data management process, is neglected by a large number of researchers. The images are mostly not described or annotated and when this happens, researchers don't provide much detail about this.