Abstract-Graphical user interfaces are difficult to test: automated tests are hard to create and maintain, while manual tests are time-consuming, expensive and hard to integrate in a continuous testing process. In this paper, we show that it is possible to crowdsource GUI tests, that is, to outsource them to individuals drawn from a very large pool of workers on the Internet. This is made possible by instantiating virtual machines running the system under test and letting testers access the VMs through their web browsers, enabling semi-automated continuous testing of GUIs and usability experiments with large numbers of participants at low cost. Several large experiments on the Amazon Mechanical Turk demonstrate that our approach is technically feasible and sufficiently reliable.
Humanity’s notion of trust is shaped by new platforms operating in the emerging sharing economy, acting as intermediate matchmaker for ride sharing, housing facilities or freelance labour, effectively creating an environment where strangers trust each other. While millions of people worldwide rely on online sharing activities, such services are often facilitated by a few predatory companies, managing trust relations. This centralization of responsibility raises questions about ethical and political issues like regulatory compliance, data portability and monopolistic behaviour. Recently, blockchain technology has gathered a significant amount of support and adoption, due to its inherent decentralized and tamper-proof structure. We present a blockchain-powered blueprint for a shared and public
This paper studies a new way of accessing videos in a nonlinear fashion. Existing non-linear access methods allow users to jump into videos at points that depict specific visual concepts or that are likely to elicit affective reactions. We believe that deep-link comments, which occur unprompted on social video sharing platforms, offer a new opportunity beyond existing methods. With deep-link comments, viewers express themselves about a particular moment in a video by including a time-code. Deep-link comments are special because they reflect viewer perceptions of noteworthiness, that include, but extend beyond depicted conceptual content and induced affective reactions. Based on deep-link comments collected from YouTube, we develop a Viewer Expressive Reaction Variety (VERV) taxonomy that captures how viewers deep-link. We validate the taxonomy with a user study on a crowdsourcing platform and discuss how it extends conventional relevance criteria. We carry out experiments which show that deep-link comments can be automatically filtered and sorted into VERV categories.
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