ASE 2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017), October 30 – November 3, 2017, Urbana-Champaign, IL, USA

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Recommender Systems
Technical Research
Recommending Crowdsourced Software Developers in Consideration of Skill Improvement
Zizhe Wang, Hailong Sun, Yang Fu, and Luting Ye
(Beihang University, China)
Abstract: 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 conduct more development tasks. Prior studies on crowdsourcing developer recommendation do not consider the changing of skills, which can underestimate developers' skill to fulfill a task. In this work, we first conducted an empirical study of the performance of 7,620 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 crowdsourcing software development.

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Time stamp: 2019-06-19T15:52:05+02:00