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| Pricing crowdsourcing-based software development tasks | |
| Mao, Ke (1); Yang, Ye (1); Li, Mingshu (1); Harman, Mark (3) | |
| 2013 | |
| Conference Name | 2013 35th International Conference on Software Engineering, ICSE 2013 |
| Pages | 1205-1208 |
| Conference Date | May 18, 2013 - May 26, 2013 |
| Conference Place | San Francisco, CA, United states |
| Indexed Type | CPCI ; EI |
| Publish Place | IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States |
| 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 |
| 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.; 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. |
| Keyword | Crowdsourcing Pricing Software Measurement |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16537 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Mao, Ke ,Yang, Ye ,Li, Mingshu ,et al. Pricing crowdsourcing-based software development tasks[C]. IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States,2013:1205-1208. |
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