Implicit feedback is collecting information about software usage to understand how and when the software is used. This research tackles implicit feedback in Software Product Lines (SPLs). The need for platform-centric feedback makes SPL feedback depart from one-off-application feedback in both the artefact to be tracked (the platform vs the variant) as well as the tracking approach (indirect coding vs direct coding). Traditionally, product feedback is achieved by embedding ‘usage trackers’ into the software’s code. Yet, products are now members of the SPL portfolio, and hence, this approach conflicts with one of the main SPL tenants: reducing, if not eliminating, coding directly into the variant’s code. Thus, we advocate for Product Derivation to be subject to a second transformation that precedes the construction of the variant based on the configuration model. This approach is tested through FEACKER, an extension to pure::variants. We resorted to a TAM evaluation on pure-systems GmbH employees(n=8). Observed divergences were next tackled through a focus group (n=3). The results reveal agreement in the interest in conducting feedback analysis at the platform level (perceived usefulness) while regarding FEACKER as a seamless extension to pure::variants’ gestures (perceived ease of use).

The Web Accessibility Guidelines are designed to help developers ensure that web content is accessible to all users. These guidelines provide the foundation for evaluation tools that automate inspection processes. However, due to the heterogeneity of these guidelines and the subjectivity involved in their evaluation, humans are still necessary for the process. As a result, evaluating accessibility becomes a collaborative endeavor wherein different human experts and tools interact. Despite quickly being noticed by the W3C, it has largely been overlooked in the existing literature. Tool vendors often focus on providing a thorough evaluation rather than importing, integrating, and combining results from diverse sources. This paper examines an EARL-based document-centric workflow. It introduces a dedicated editor for EARL documents that accounts for the life-cycle of EARL documents where evaluation episodes feedback on each other. Expert evaluations were conducted (n = 5 experts), not so much about the tool itself but its ability to facilitate a collaborative approach.KeywordsWeb engineeringWeb accessibilityWeb accessibility evaluationBrowser extensionAggregation

This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.