一句话：提出众包开发者队友偏好模型与启发式算法，为个体开发者推荐合适协作者。
现有众包软件开发平台多采用竞赛模式，并通常假设开发者独立贡献，缺乏对团队组建的有效支持。已有团队推荐研究多为任务推荐最优团队，而非为特定开发者寻找合作者。本文首先对 Kaggle 进行实证研究，发现开发者个人队友偏好主要受三类因素影响；随后提出协作意愿模型来刻画队友偏好，将队友推荐形式化为优化问题，并设计启发式算法为开发者寻找合适队友。最后在 Kaggle 数据集上开展实验，验证所提方法的有效性。
Most crowdsourced software development platforms adopt contest paradigm to solicit contributions from the community. To attain competitiveness in complex tasks, crowdsourced software developers often choose to work with others collaboratively. However, existing crowdsourcing platforms generally assume independent contributions from developers and do not provide effective support for team formation. Prior studies on team recommendation aim at optimizing task outcomes by recommending the most suitable team for a task instead of finding appropriate collaborators for a specific person. In this work, we are concerned with teammate recommendation for crowdsourcing developers. First, we present the results of an empirical study of Kaggle, which shows that developers’personal teammate preferences are mainly affected by three factors. Second, we give a collaboration willingness model to characterize developers’ teammate preferences and formulate teammate recommendation as an optimization problem. Then we design a heuristic algorithm to find suitable teammates for a developer. Finally, we have conducted a set of experiments on a Kaggle dataset to evaluate the effectiveness of our approach.
DOI 原文 ·
@inproceedings{paperbot4786,
title = {Personalized teammate recommendation for crowdsourced software developers},
author = {Luting Ye and Hailong Sun and Xu Wang and Jiaruijue Wang},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3240472}
}