@inproceedings{887af7a28fc54ca680737af2eebcbe99,
title = "Recommending crowdsourced software developers in consideration of skill improvement",
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.",
keywords = "Crowdsourcing, Topcoder, recommender systems, software development",
author = "Zizhe Wang and Hailong Sun and Yang Fu and Luting Ye",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017 ; Conference date: 30-10-2017 Through 03-11-2017",
year = "2017",
month = nov,
day = "20",
doi = "10.1109/ASE.2017.8115682",
language = "英语",
series = "ASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "717--722",
editor = "Nguyen, \{Tien N.\} and Grigore Rosu and \{Di Penta\}, Massimiliano",
booktitle = "ASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering",
address = "美国",
}