White paper on crowdsourced network and QoE measurements – definitions, use cases and challenges

The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements

CAN LAYMEN OUTPERFORM EXPERTS? THE EFFECTS OF USER EXPERTISE AND TASK DESIGN IN CROWDSOURCED SOFTWARE TESTING

In recent years, crowdsourcing has increasingly gained attention as a powerful sourcing mechanism for problem-solving in organizations. Depending on the type of activity addressed by crowdsourcing, the complexity of the tasks and the role of the crowdworkers may differ substantially. It is crucial that the tasks are designed and allocated according to the capabilities of the targeted crowds. In this pa-per, we outline our research in progress which is concerned with the effects of task complexity and user expertise on performance in crowdsourced software testing. We conduct an experiment and gath-er empirical data from expert and novice crowds that perform different software testing tasks of vary-ing degrees of complexity. Our expected contribution is twofold. For crowdsourcing in general, we aim at providing valuable insights for the process of framing and allocating tasks to crowds in ways that increase the crowdworkers’ performance. Secondly, we intend to improve the configuration of crowdsourced software testing initiatives. More precisely, the results are expected to show practition-ers what types of testing tasks should be assigned to which group of dedicated crowdworkers. In this vein, we deliver valuable decision support for both crowdsourcers and intermediaries to enhance the performance of their crowdsourcing initiatives

A survey of the use of crowdsourcing in software engineering

The term 'crowdsourcing' was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering