TY - GEN
T1 - Personalized teammate recommendation for crowdsourced software developers
AU - Ye, Luting
AU - Wang, Xu
AU - Sun, Hailong
AU - Wang, Jiaruijue
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/9/3
Y1 - 2018/9/3
N2 - 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 the teammate recommendation problem as an optimization problem. Then we design an approximation 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.
AB - 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 the teammate recommendation problem as an optimization problem. Then we design an approximation 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.
KW - Collaboration willingness
KW - Crowdsourcing
KW - Teammate recommendation
UR - https://www.scopus.com/pages/publications/85056557766
U2 - 10.1145/3238147.3240472
DO - 10.1145/3238147.3240472
M3 - 会议稿件
AN - SCOPUS:85056557766
T3 - ASE 2018 - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
SP - 808
EP - 813
BT - ASE 2018 - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
A2 - Kastner, Christian
A2 - Huchard, Marianne
A2 - Fraser, Gordon
PB - Association for Computing Machinery, Inc
T2 - 33rd IEEE/ACM International Conference on Automated Software Engineering, ASE 2018
Y2 - 3 September 2018 through 7 September 2018
ER -