摘要
Gitcoin是一个基于开源社区GitHub的众包平台。在Gitcoin中,项目团队可以发布开发任务,开发者选择感兴趣的任务并注册,发布者选择合适的开发者完成任务并发放赏金。但是一些任务因缺乏注册者而失败,部分任务未能合格完成,顺利完成的任务也面临开发者注册间隔时间长的问题。因此,需要一种开发者推荐方法,快速为众包任务发现合适的开发人员,缩短开发者注册众包任务的时间,发现潜在合适的开发者并激励其注册,促进众包任务顺利完成。文中提出了一种基于LGBM分类算法的开发者推荐方法DEVRec(Developer Recommendation)。该方法提取任务特征、开发者特征、开发者和任务的关系特征,使用LGBM分类算法进行二分类,计算开发者注册任务的概率,最终得到众包任务的推荐人员列表。为了评估推荐效果,获取Gitcoin的1599个已完成众包任务、343名任务发布者和1605名开发者。实验结果显示,与对比方法Policy Model相比,DEVRec前1位、前3位、前5位和前10位推荐的准确度及MRR指标分别提高了73.11%,119.07%,86.55%,29.24%和62.27%。
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,1599 completed crowdsourcing tasks,343 publishers,and 1605 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.
作者
蒋竞
平源
吴秋迪
张莉
JIANG Jing;PING Yuan;WU Qiu-di;ZHANG Li(School of Computer Science and Engineering,Beihang University,Beijing 100191,China)
出处
《计算机科学》
CSCD
北大核心
2022年第12期99-108,共10页
Computer Science
基金
科技创新2030——“新一代人工智能”重大项目(2018AAA0102304)
国家自然科学基金(62177003)
中央高校基本科研业务费专项资金(YWF-20-BJ-J-1018)。
关键词
开源软件
开发者推荐
众包开发
特征提取
机器学习
Open-source software
Developer recommendation
Crowdsourcing development
Feature extraction
Machine learning
