As an emerging and promising approach, crowdsourcing-based software development has become popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the ‘wining’ solution with respect to their desired quality levels. However, due to lack of a central mechanism for team formation, continuity in the developer’s work on consecutive tasks and risk of noise in submissions of a contest, there is a gap between the requesters of a domain and their quality concerns related to the adaptation of a crowdsourcing-based software development platform. In order to address concerns and aid requesters, we describe three measures; Quality of Registrant Developers (QRD), Quality of Contest (QC) and Quality of Support (QS) to compute and predict the quality of a crowdsourcing-based platform through historical information on its completed tasks. We evaluate the capacity of the QRD, QC and QS as assessors to predict the quality. Subsequently, we implement a crawler to mine the information of completed development tasks from the TopCoder platform to inspect the proposed measures. The promising results of our QRD, QC, and QS measures suggest to use the proposed measures to the requesters and researchers of other domains such as pharmaceutical research and development, in order to investigate and predict the quality of crowdsourcing-based software development platforms.

中文翻译：

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众包软件开发平台的基于知识的质量分析

作为一种新兴且有前途的方法，由于有才华的开发人员参与了竞赛，并且提高了请求者（或客户）选择“获胜”解决方案的能力，基于众包的软件开发已在许多领域中流行起来。关于他们期望的质量水平。但是，由于缺乏团队形成的中央机制，开发人员在连续任务上的工作连续性以及提交竞赛中存在噪音的风险，因此域的请求者与其适应性相关的质量问题之间存在差距。基于众包的软件开发平台。为了解决关注点和援助请求者，我们描述了三种措施：注册开发者质量（QRD），竞赛质量（QC）和支持质量（QS），以通过有关已完成任务的历史信息来计算和预测基于众包的平台的质量。我们评估QRD，QC和QS作为评估者预测质量的能力。随后，我们实现了一个搜寻器，以从TopCoder平台中挖掘完成的开发任务的信息，以检查建议的措施。QRD，QC和QS措施的可喜结果表明，应将拟议措施用于药物开发等其他领域的请求者和研究人员，以调查和预测基于众包的软件开发平台的质量。QC和QS作为评估人员来预测质量。随后，我们实现了一个搜寻器，以从TopCoder平台中挖掘完成的开发任务的信息，以检查建议的措施。QRD，QC和QS措施的可喜结果表明，应将拟议措施用于药物开发等其他领域的请求者和研究人员，以调查和预测基于众包的软件开发平台的质量。QC和QS作为评估人员来预测质量。随后，我们实现了一个搜寻器，以从TopCoder平台中挖掘完成的开发任务的信息，以检查建议的措施。QRD，QC和QS措施的可喜结果表明，应将拟议措施用于药物开发等其他领域的请求者和研究人员，以调查和预测基于众包的软件开发平台的质量。