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Title:
Pricing crowdsourcing-based software development tasks
Author: Mao, Ke (1) ; Yang, Ye (1) ; Li, Mingshu (1) ; Harman, Mark (3)
Conference Name: 2013 35th International Conference on Software Engineering, ICSE 2013
Conference Date: May 18, 2013 - May 26, 2013
Issued Date: 2013
Conference Place: San Francisco, CA, United states
Keyword: crowdsourcing ; pricing ; software measurement
Publish Place: IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States
Indexed Type: CPCI ; EI
ISSN: 2705257
ISBN: 9781467330763
Department: (1) Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) University of Chinese, Academy of Sciences, Beijing, China; (3) Dept. of Computer Science, University of College London, London, United Kingdom
Abstract: Many organisations have turned to crowdsource their software development projects. This raises important pricing questions, a problem that has not previously been addressed for the emerging crowdsourcing development paradigm. We address this problem by introducing 16 cost drivers for crowdsourced development activities and evaluate 12 predictive pricing models using 4 popular performance measures. We evaluate our predictive models on TopCoder, the largest current crowdsourcing platform for software development. We analyse all 5,910 software development tasks (for which partial data is available), using these to extract our proposed cost drivers. We evaluate our predictive models using the 490 completed projects (for which full details are available). Our results provide evidence to support our primary finding that useful prediction quality is achievable (Pred(30)>0.8). We also show that simple actionable advice can be extracted from our models to assist the 430,000 developers who are members of the TopCoder software development market. © 2013 IEEE.
English Abstract: Many organisations have turned to crowdsource their software development projects. This raises important pricing questions, a problem that has not previously been addressed for the emerging crowdsourcing development paradigm. We address this problem by introducing 16 cost drivers for crowdsourced development activities and evaluate 12 predictive pricing models using 4 popular performance measures. We evaluate our predictive models on TopCoder, the largest current crowdsourcing platform for software development. We analyse all 5,910 software development tasks (for which partial data is available), using these to extract our proposed cost drivers. We evaluate our predictive models using the 490 completed projects (for which full details are available). Our results provide evidence to support our primary finding that useful prediction quality is achievable (Pred(30)>0.8). We also show that simple actionable advice can be extracted from our models to assist the 430,000 developers who are members of the TopCoder software development market. © 2013 IEEE.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16537
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Mao, Ke ,Yang, Ye ,Li, Mingshu ,et al. Pricing crowdsourcing-based software development tasks[C]. 见:2013 35th International Conference on Software Engineering, ICSE 2013. San Francisco, CA, United states. May 18, 2013 - May 26, 2013.
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