%0 Journal Article
%T 众包测试研究综述<br>Research Review of Crowdsourcing Testing
%A 刘益玮
%A 李瑛
%A 赵悦彤
%J Software Engineering and Applications
%P 295-301
%@ 2325-2278
%D 2024
%I Hans Publishing
%R 10.12677/sea.2024.133029
%X 众包测试利用众包和云平台优势，整合大规模的测试人员协同完成软件测试任务，有效解决了传统测试中人力资源不足以及无法大规模获取用户真实反馈的典型问题，从而获得更全面、多样化的测试结果。本文通过系统分析近年来众包测试研究文献，总结了众包测试的基本流程及其特点，重点梳理了众包平台激励机制、众包测试推荐机制以及测试报告自动生成三方面的关键技术，并完成了对现有技术的系统归纳与对比分析。最后，探讨了众包测试面临的挑战，如激励机制的动态调整、更个性化的任务推荐、根据报告生成测试用例等；并对未来的研究方向进行了展望，特别是在大语言模型辅助下带来的机遇与挑战。<br />
Crowdsourcing testing uses the advantages of crowdsourcing and cloud platform to integrate large-scale testers to complete software testing tasks, effectively solving the typical problems of insufficient human resources in traditional testing and the inability to obtain real feedback from users on a large scale, so as to obtain more comprehensive and diversified test results. By systematically analyzing the research literature on crowdsourcing testing in recent years, this paper summarizes the basic process and characteristics of crowdsourcing testing, focuses on the key technologies of crowdsourcing platform incentive mechanism, crowdsourcing test recommendation mechanism and automatic generation of test reports, and completes the systematic induction and comparative analysis of existing technologies. Finally, the challenges of crowdsourcing testing are discussed, such as dynamic adjustment of incentive mechanisms, more personalized task recommendations, generating test cases based on reports, etc. The future research direction is also prospected, especially the opportunities and challenges brought by the aid of large language models.
%K 众包测试，激励机制，任务推荐，测试报告<br>Crowdsourced Testing
%K Incentive Mechanism
%K Task Recommendation
%K Test Report
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=89355