期刊文献+

软件众包任务发布优先级计算方法 被引量:2

Priority calculation method of software crowdsourcing task release
下载PDF
导出
摘要 针对现有软件众包平台对任务发布次序考虑不足的问题,提出一种基于任务发布者权重与任务权重的软件众包任务发布优先级(TRP)计算方法。首先,基于半正弦曲线的时间权重函数度量任务发布者的活跃度及其任务累积成交额,以此计算任务发布者权重;然后,根据系统架构图和数据流图度量模块复杂度、设计复杂度和数据复杂度,得到任务复杂度,并结合任务报价及任务期限,计算任务效益因子和任务紧急程度因子,计算任务权重;最后,根据任务发布者权重和任务权重计算任务发布优先级。实验结果表明,该算法不仅具有较高的有效性和合理性,而且任务成功分配率最高可达98%。 Aiming at the problem that the existing software crowdsourcing platforms do not consider the order of task release, a method of calculating Task Release Priority (TRP) of software crowdsourcing based on task publisher weight and task weight was proposed. Firstly, a time weight function based on semi-sinusoidal curve was used to measure the activity of the task publisher and the cumulative turnover of the task, so as to calculate the weight of the task publisher. Secondly, the task complexity was calculated according to the system architecture diagram and data flow diagram to measure module complexity, design complexity and data complexity, and the task benefit factor and task emergency factor were calculated based on task quotation and task duration. In this way, the task weight was calculated. Finally, the task publishing priority would be given according to task publisher weight and task weight. The experimental results show that the proposed algorithm not only is effective and reasonable, but also has a maximum success rate of 98%.
作者 赵焜松 余敦辉 张万山 ZHAO Kunsong;YU Dunhui;ZHANG Wanshan(School of Computer Science and Information Engineering,Hubei University,Wuhan Hubei 430062,China;Educational Informationalization Engineering Research Center of Hubei Province,Wuhan Hubei 430062,China)
出处 《计算机应用》 CSCD 北大核心 2018年第7期2032-2036,共5页 journal of Computer Applications
基金 国家973计划项目(2014CB340404) 国家自然科学基金资助项目(61373037 61672387)~~
关键词 软件众包 任务排序 任务分配 半正弦曲线 软件复杂度 software crowdsourcing task scheduling task allocation half-sinusoid software complexity
  • 相关文献

参考文献12

二级参考文献301

  • 1Howe Jeff. The rise of crowdsourcing. Wired, 2006, 14(6) : 176-183.
  • 2Callison-Burch C. Fast, cheap, and creative: Evaluating translation quality using Amazon- s mechanical turk//Pro- ceedings of of the Conference on Empirical Methods in Natu- ral Language Processing. Singapore, 2009: 286-295.
  • 3Yan Tingxin, Kumar V, Ganesan D. CrowdSearch: Exploi ting crowds for accurate real-time image search on mobile phones//Proeeedings of the International Conference on Mo- bile Systems, Applications, and Services. San Francisco, USA, 2010:77-90.
  • 4Alonso O, Rose D E, Stewart B. Crowdsoureing for rele- vance evaluation. Journal of SIGIR Forum (SIGIR), 2008, 42(2) : 9-15.
  • 5Alonso O, Mizzaro S. Can we get rid of TREC assessors? Using mechanical turk for relevance assessment//Proceedings of the SIGIR Workshop on the Future of IR Evaluation. Boston, Massachusetts, USA, 2009:15-16.
  • 6Lease M, Carvalho V R, Yilmaz E. Crowdsoureing for search and data mining. Journal of SIGIR Forum (SIGIR), 2011, 45(1): 18-24.
  • 7Kamath K Y, Caverlee J. Transient crowd discovery on the real-time social Web//Proceedings of the WSDM. Hong Kong, China, 2011:585-594.
  • 8Castillo C, Mendoza M, Poblete B. Information credibility on twitter//Proceedings of the WWW. Hyderabad, India, 2011:675-684.
  • 9Bigham J P, Jayant C, Ji H, et al. VizWiz: Nearly real-time answers to visual questions//Proceedings of the 13IST. New York City, USA, 2010. 333-342.
  • 10Hofmann T, Puzicha J. Statistical models for co-occurrence data. Massachusetts Institute of Technology Artificial Intelli- gence Laboratory, Massachusetts State of USA: Technical Report AIM- 1625, CBCL-159, 1998.

共引文献294

同被引文献26

引证文献2

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部