一句话：论文倡导在众包测试过程中进行上下文感知的动态工作者推荐，以缩短无新缺陷产生的非产出窗口。
现有众包测试工作者推荐研究多在新任务开始时依据初始上下文做一次性推荐，难以应对测试过程中动态变化的需求。本文主张面向众包测试过程进行上下文感知的众包工作者推荐。作者通过一项 pilot study 分析众包测试过程，发现长时间非产出窗口普遍存在，即在连续多份测试报告中未发现新缺陷。这一现象表明，若能根据动态上下文在过程中推荐合适工作者，就有机会缩短非产出窗口，从而加速众包测试、以更少工作者发现更多缺陷。摘要未详细给出具体推荐模型及完整实验评估，主要通过 pilot study 论证该研究方向的必要性。
Identifying and optimizing open participation is essential to the success of open software development. Existing studies highlighted the importance of worker recommendation for crowdtesting tasks in order to detect more bugs with fewer workers. However, these studies mainly focus on one-time recommendations with respect to the initial context at the beginning of a new task. This paper argues the need for in-process crowdtesting worker recommendation. We motivate this study through a pilot study, revealing the prevalence of long-sized non-yielding windows, i.e., no new bugs are revealed in consecutive test reports during the process of a crowdtesting task. This indicates the potential opportunity for accelerating crowdtesting by recommending appropriate workers in a dynamic manner, so that the non-yielding windows could be shortened.
DOI 原文 ·
@inproceedings{paperbot5575,
title = {Context-aware in-process crowdworker recommendation},
author = {Junjie Wang and Ye Yang and Song Wang and Yuanzhe Hu and Dandan Wang and Qing Wang},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380380}
}