Developer Recommendation Method for Crowdsourcing Tasks in Open Source Community

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.

Keywords

• open-source software|developer recommendation|crowdsourcing development|feature extraction|machine learning