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ASE 2017

Recommending crowdsourced software developers in consideration of skill improvement

Zizhe Wang, Hailong Sun, Yang Fu, Luting Ye

DOI 10.1109/ase.2017.8115682dblp

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 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.

BibTeX
@inproceedings{Wang-al:ASE17,
  author    = {Zizhe Wang and
               Hailong Sun and
               Yang Fu and
               Luting Ye},
  title     = {Recommending crowdsourced software developers in consideration of skill improvement},
  booktitle = {ASE},
  pages     = {717--722},
  publisher = {{IEEE} Computer Society},
  year      = {2017},
}

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