Pricing Crowdsourcing-Based Software Development Tasks
Ke Mao, Ye Yang, Mingshu Li, and Mark Harman
Institute of Software of the Chinese Academy of Sciences, China; University College London, UK
Track: New Ideas and Emerging Results
Session: Collaborative Development
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.