NDLI: Improving Crowdsourcing Efficiency Based on Division Strategy

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Improving Crowdsourcing Efficiency Based on Division Strategy

Content Provider	ACM Digital Library
Author	Jiang, Huan Matsubara, Shigeo
Abstract	This paper examines the efficiency in crowd sourcing, especially crowd sourcing for the software bug detection. Crowd sourcing has recently emerged as a lucrative paradigm for leveraging the collective intelligence of crowds. However, it has inherent weakness that a simple reward setting causes an uneven distribution of workers on each task, which reduces the efficiency of solving the tasks. A challenge is that the system designer is not allowed to set the reward to the arbitrary value because so-called "market wages" exist and if the reward is set to the value lower than the market wage, such a task fails to attract the sufficient number of workers. To solve this problem, we focus on the division strategy that divides the crowds into different groups and the workers compete with each other among the same group. We have developed a model that crowds write their codes independently and then try to find bugs in the codes written by the others. Next, we examine two division strategy, random grouping and ability grouping by analyzing the equilibrium behavior of each worker and carrying out simulations. The results show that the division strategy affect the efficiency of crowd sourcing bug detection and the random grouping leads to a higher efficiency compared to the ability grouping.
Starting Page	425
Ending Page	429
Page Count	5
File Format	PDF
ISBN	9780769548807
DOI	10.1109/WI-IAT.2012.86
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-12-04
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing efficiency division strategy software bug detection
Content Type	Text
Resource Type	Article

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