基于活跃时间分组的软件众包工人选择机制
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摘要
针对现有的软件众包工人选择机制对工人间协同开发考虑不足的问题,在竞标模式的基础上提出一种基于活跃时间分组的软件众包工人选择机制。首先,基于活跃时间将众包工人划分为多个协同开发组;然后,根据组内工人开发能力和协同因子计算协同工作组权重;最后,选定权重最大的协同工作组为最优工作组,并根据模块复杂度为每个任务模块从该组内选择最适合的工人。实验结果表明,该机制相比能力优先选择方法在工人平均能力上仅有0. 57%的差距,同时因为保证了工人间的协同而使项目风险平均降低了32%,能有效指导需多人协同进行的众包软件任务的工人选择。
Concerning the problem that existing software crowdsourcing worker selection mechanisms do not consider the collaboration among workers,a crowdsourcing worker selection mechanism with bidding model based on active time grouping was proposed.Firstly,crowd-sourced workers were divided into multiple collaborative working groups based on active time.Then,the weights of the working groups were calculated according to the development capabilities of the workers in the group and collaboration factors.Finally,the collaborative working group with the highest weight was selected as the optimal working group,and the most suitable worker from this group was selected for each task module according to the complexity of the module.The experimental results show that the proposed mechanism has a gap of only 0.57% in the average worker ability compared to the ability only allocation method.At the same time,it reduces the project risk by an average of 32% due to the ensurence of the cooperation between workers,which can effectively guide the selection of workers for multi-person collaborative crowdsourcing software tasks.
Concerning the problem that existing software crowdsourcing worker selection mechanisms do not consider the collaboration among workers,a crowdsourcing worker selection mechanism with bidding model based on active time grouping was proposed.Firstly,crowd-sourced workers were divided into multiple collaborative working groups based on active time.Then,the weights of the working groups were calculated according to the development capabilities of the workers in the group and collaboration factors.Finally,the collaborative working group with the highest weight was selected as the optimal working group,and the most suitable worker from this group was selected for each task module according to the complexity of the module.The experimental results show that the proposed mechanism has a gap of only 0.57% in the average worker ability compared to the ability only allocation method.At the same time,it reduces the project risk by an average of 32% due to the ensurence of the cooperation between workers,which can effectively guide the selection of workers for multi-person collaborative crowdsourcing software tasks.
引文
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[4]ZHANG L,ZHANG H.Research of crowdsourcing model based on case study[C]//Proceedings of the 2011 International Conference on Service Systems and Service Management.Piscataway,NJ:IEEE,2011:1-5.
[5]STOL K J,FITZGERALD B.Researching crowdsourcing software development:perspectives and concerns[C]//Proceedings of the2014 1st International Workshop on Crowdsourcing in Software Engineering.New York:ACM.2014:7-10.
[6]PENG X,BABAR M A,EBERT C.Collaborative software development platforms for crowdsourcing[J].IEEE Software,2014,31(2):30-36.
[7]DWARAKANATH A,CHINTALA U,SHRIKANTH N C,et al.CrowdBuild:a methodology for enterprise software development using crowdsourcing[C]//Proceedings of the 2015 2nd International Workshop on Crowdsourcing in Software Engineering.Piscataway,NJ:IEEE,2015:8-14.
[8]TAJEDIN H,NEVO D.Determinants of success in crowdsourcing software development[C]//Proceedings of the 2013 Annual Conference on Computers and People Research.New York:ACM.2013:173-178.
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[13]MAO K,YANG Y,WANG Q,et al.Developer recommendation for crowdsourced software development tasks[C]//Proceedings of the2015 IEEE Symposium on Service-Oriented System Engineering.Washington,DC:IEEE Computer Society,2015:347-356.
[14]SHAO W,WANG X,JIAO W.A developer recommendation framework in software crowdsourcing development[C]//Proceedings of the 2016 Software Engineering and Methodology for Emerging Domains,CCIS 675.Singapore:Springer,2016:151-164.
[15]ZHU J,SHEN B,HU F.A learning to rank framework for developer recommendation in software crowdsourcing[C]//Proceedings of the 2015 Asia-Pacific Software Engineering Conference.Washington,DC:IEEE Computer Society,2016:285-292.
[16]WANG Z,SUN H,FU Y,et al.Recommending crowdsourced software developers in consideration of skill improvement[C]//Proceedings of the 2017 32nd IEEE/ACM International Conference on Automated Software Engineering.Washington,DC:IEEEComputer Society,2017:717-722.
[17]BISCHOFBERGER W R,KOFLER T,MATZEL K-U,et al.Computer supported cooperative software engineering with BeyondSniff[C]//Proceedings of the 1995 Software Engineering Environments.Washington,DC:IEEE Computer Society,1995:135-143.
[18]高铭,王毅.众包项目风险评估模型研究[J].管理现代化,2016,36(3):105-107.(GAO M,WANG Y.Research on risk assessment model of crowdsourcing project[J].Modernization of Management,2016,36(3):105-107.)