Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (12): 99-108.doi: 10.11896/jsjkx.220400289

• Computer Software • Previous Articles     Next Articles

Developer Recommendation Method for Crowdsourcing Tasks in Open Source Community

JIANG Jing, PING Yuan, WU Qiu-di, ZHANG Li   

  1. School of Computer Science and Engineering,Beihang University,Beijing 100191,China
  • Received:2022-04-28 Revised:2022-06-10 Published:2022-12-14
  • About author:JIANG Jing,born in 1985,Ph.D,asso-ciate professor.Her main research in-terests include intelligent software engineering,empirical software enginee-ring,open source software and software repository mining.ZHANG Li,born in 1968,Ph.D,professor.Her main research interests include software modeling and analysis,requirement engineering,empirical software engineering and software architecture.
  • Supported by:
    National Key Research and Development Program of China(2018AAA0102304), National Natural Science Foundation of China(62177003) and Fundamental Research Funds for the Central Universities of Ministry of Education of China(YWF-20-BJ-J-1018).

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Abstract: Gitcoin is a crowdsourcing platform based on open-source community GitHub.In Gitcoin,project teams can release development tasks.The developers select the task they are interested in to register,and the publisher selects the appropriate deve-loper to complete the task and offers a reward.But some tasks fail because of a lack of registrants.Some tasks are not performed properly.Successfully completed tasks also face the problem of long developer registration intervals.Therefore,a developer re-commendation method is needed to quickly find suitable developers for crowdsourcing tasks,shorten the time for developers to register for crowdsourcing tasks,find potential suitable developers and motivate them to register,so as to promote the successful completion of crowdsourcing tasks.A developer recommendation system DEVRec based on the LGBM classification algorithm is proposed in this paper.Firstly,the task-related characteristics,developer-related characteristics,and the relationship between developers and tasks in the crowd-sourcing task assignment records are extracted.Then the LGBM classification algorithm is used for binary classification.The probability of a developer registering the task is given,and finally the list of recommended people for the task is provided.To evaluate the recommendation effect,1 599 completed crowdsourcing tasks,343 publishers,and 1 605 deve-lopers are crawled from Gitcoin platform.Experimental results show that,compared with the Policy Model,the recommendation accuracy and MRR index of the top 1,top3,top5 and top10 of DEVRec improves by 73.11%,119.07%,86.55%,29.24% and 62.27% respectively.

Key words: Open-source software, Developer recommendation, Crowdsourcing development, Feature extraction, Machine learning

CLC Number: 

  • TP311
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