A recommender system for developer onboarding

Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings Pub Date : 2018-05-27 DOI:10.1145/3183440.3194989

Chao Liu, Dan Yang, Xiaohong Zhang, Haibo Hu, J. Barson, Baishakhi Ray

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引用次数: 4

Abstract

Successfully onboarding open source projects in GitHub is difficult for developers, because it is time-consuming for them to search an expected project by a few query words from numerous repositories, and developers suffer from various social and technical barriers in joined projects. Frequently failed onboarding postpones developers' development schedule, and the evolutionary progress of open source projects. To mitigate developers' costly efforts for onboarding, we propose a ranking model NNLRank (Neural Network for List-wise Ranking) to recommend projects that developers are likely to contribute many commits. Based on 9 measured project features, NNLRank learns a ranking function (represented by a neural network, optimized by a list-wise ranking loss function) to score a list of candidate projects, where top-n scored candidates are recommended to a target developer. We evaluate NNLRank by 2044 succeeded onboarding decisions from GitHub developers, comparing with a related model LP (Link Prediction), and 3 other typical ranking models. Results show that NNLRank can provide developers with effective recommendation, substantially outperforming baselines.

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开发人员入职推荐系统

对于开发人员来说，成功地在GitHub中导入开源项目是很困难的，因为他们要从众多的存储库中通过几个查询词来搜索期望的项目是很耗时的，而且开发人员在加入项目时还会遇到各种社会和技术障碍。经常失败的入职推迟了开发人员的开发进度，也推迟了开源项目的演进进度。为了减轻开发人员在入职过程中付出的昂贵努力，我们提出了一个排名模型NNLRank(列表智能排名的神经网络)来推荐开发人员可能贡献很多提交的项目。基于9个测量的项目特征，NNLRank学习一个排名函数(由神经网络表示，由列表排序损失函数优化)来对候选项目列表进行评分，其中得分最高的n个候选项目被推荐给目标开发人员。通过与相关模型LP (Link Prediction)和其他3个典型排名模型进行比较，我们评估了2044个来自GitHub开发人员的成功入职决策。结果表明，NNLRank可以为开发人员提供有效的推荐，大大优于基线。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

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