Selenium is often considered the de-facto standard framework for end-to-end web testing nowadays. It allows practitioners to drive web browsers (such as Chrome, Firefox, Edge, or Opera) in an automated fashion using different language bindings (such as Java, Python, or JavaScript, among others). The term ecosystem, referring to the open-source software domain, includes various components, tools, and other interrelated elements sharing the same technological background. This article presents a descriptive survey aimed to understand how the community uses Selenium and its ecosystem. This survey is structured in seven categories: Selenium foundations, test development, system under test, test infrastructure, other frameworks, community, and personal experience. In light of the current state of Selenium, we analyze future challenges and opportunities around it.

WebRTC comprises a set of technologies and standards that provide real-time communication with web browsers, simplifying the embedding of voice and video communication in web applications and mobile devices. The perceived quality of WebRTC communication can be measured using quality of experience (QoE) indicators. QoE is defined as the degree of delight or annoyance of the user with an application or service. This paper is focused on the QoE assessment of WebRTC-based applications and its contribution is threefold. First, an analysis of how WebRTC topologies affect the quality perceived by users is provided. Second, a group of Key Performance Indicators for estimating the QoE of WebRTC users is proposed. Finally, a systematic survey of the literature on QoE assessment in the WebRTC arena is presented.

WebRTC is a set of standard technologies that allows exchanging video and audio in real time on the Web. As with other media-related applications, the user-perceived audiovisual quality can be estimated using Quality of Experience (QoE) measurements. This paper analyses the behavior of different objective Full-Reference (FR) models for video and audio in WebRTC applications. FR models calculate the video and audio quality by comparing some original media reference with the degraded signal. To compute these models, we have created an open-source benchmark in which different types of reference media inputs are sent browser to browser while simulating different kinds of network conditions in terms of packet loss and jitter. Our benchmark provides recording capabilities of the impairment WebRTC streams. Then, we use different existing FR metrics for video (VMAF, VIFp, SSIM, MS-SSIM, PSNR, PSNR-HVS, and PSNR-HVS-M) and audio (PESQ, ViSQOL, and POLQA) recordings together with their references. Moreover, we use the same recordings to carry out a subjective analysis in which real users rate the video and audio quality using a Mean Opinion Score (MOS). Finally, we calculate the correlations between the objective and subjective results to find the objective models that better correspond with the subjective outcome, which is considered the ground truth QoE. We find that some of the studied objective models, such as VMAF, VIFp, and POLQA, show a strong correlation with the subjective results in packet loss scenarios.

A privacy policy describes the operations an organization carries out on its users’ personal data and how it applies data protection principles. The automated analysis of privacy policies is a multidisciplinary research topic producing a growing but scattered body of knowledge. We address this gap by conducting a systematic mapping study which provides an overview of the field, identifies research opportunities, and suggests future research lines. Our study analyzed 39 papers from the 1097 publications found on the topic, to find what information can be automatically extracted from policies presented as textual documents, what this information is applied to, and what analysis techniques are being used. We observe that the techniques found can identify individual pieces of information from the policies with good results. However, further advances are needed to put them in context and provide valuable insight to end-users, organizations dealing with data protection laws and data protection authorities.

WebRTC is the umbrella term for several emergent technologies aimed to exchange real-time media in the Web. Like other media-related services, the perceived quality of WebRTC communication can be measured using Quality of Experience (QoE) indicators. QoE assessment methods can be classified as subjective (users’ evaluation scores) or objective (models computed as a function of different parameters). In this paper, we focus on VMAF (Video Multi-method Assessment Fusion), which is an emergent full-reference objective video quality assessment model developed by Netflix. VMAF is typically used to assess video streaming services. This paper evaluates the use of VMAF in a different type of application: WebRTC. To that aim, we present a practical use case built on the top of well-known open source technologies, such as JUnit, Selenium, Docker, and FFmpeg. In addition to VMAF, we also calculate other objective QoE video metrics such as Visual Information Fidelity in the pixel domain (VIFp), Structural Similarity (SSIM), or Peak Signal-to-Noise Ratio (PSNR) applied to a WebRTC communication in different network conditions in terms of packet loss. Finally, we compare these objective results with a subjective evaluation using a Mean Opinion Score (MOS) scale to the same WebRTC streams. As a result, we found a strong correlation of the subjective video quality perceived in WebRTC video calls with the objective results computed with VMAF and VIFp in comparison with SSIM and PSNR and their variants.

During the COVID-19 pandemic, most Higher Education Institutions (HEIs) across the globe moved towards “emergency online education”, experiencing a metamorphosis that advanced their capacities and competencies as never before. Teaching and Learning Centers (TLCs), the internal units that promote sustainable transformations, can play a key role in making this metamorphosis last. Existing models for TLCs have defined the competencies that they could help develop, focusing on teachers’, students’, and managers’ development, but have mislead aspects such as leadership, organizational processes, and infrastructures. This paper evaluates the PROF-XXI framework, which offers a holistic perspective on the competencies that TLCs should develop for supporting deep and sustainable transformations of HEIs. The framework was evaluated with 83 participants from four Latin American institutions and used for analyzing the transformation of their teaching and learning practices during the pandemic lockdown. The result of the analysis shows that the PROF-XXI framework was useful for identifying the teaching and learning competencies addressed by the institutions, their deficiencies, and their strategic changes. Specifically, this study shows that most institutions counted with training plans for teachers before this period, mainly in the competencies of digital technologies and pedagogical quality, but that other initiatives were created to reinforce them, including students’ support actions.