Recommending crowdsourced software developers in consideration of skill improvement

Finding suitable developers for a given task is critical and challenging for successful crowdsourcing software development. In practice, the development skills will be improved as developers accomplish more development tasks. Prior studies on crowdsourcing developer recommendation do not consider th...

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Bibliographic Details
Published in2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) pp. 717 - 722
Main Authors Zizhe Wang, Hailong Sun, Yang Fu, Luting Ye
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Algorithm design and analysis
Correlation
Crowdsourcing
Prediction algorithms
recommender systems
Reliability
Software
software development
Topcoder
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Summary:Finding suitable developers for a given task is critical and challenging for successful crowdsourcing software development. In practice, the development skills will be improved as developers accomplish more development tasks. Prior studies on crowdsourcing developer recommendation do not consider the changing of skills, which can underestimate developers' skills to fulfill a task. In this work, we first conducted an empirical study of the performance of 74 developers on Topcoder. With a difficulty-weighted algorithm, we re-compute the scores of each developer by eliminating the effect of task difficulty from the performance. We find out that the skill improvement of Topcoder developers can be fitted well with the negative exponential learning curve model. Second, we design a skill prediction method based on the learning curve. Then we propose a skill improvement aware framework for recommending developers for software development with crowdsourcing.
DOI:10.1109/ASE.2017.8115682