Developer Project Recommendation Model Based on CNN-LSTM in GitHub

LIAO Zhi-fang; YANG Hong-yu; SONG Tian-hui; YU Song; QI Xiao-fei

doi:10.3969/j.issn.0372-2112.2020.11.015

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Developer Project Recommendation Model Based on CNN-LSTM in GitHub

更新时间：2025-12-08

- Developer Project Recommendation Model Based on CNN-LSTM in GitHub
- Acta Electronica Sinica Vol. 48, Issue 11, Pages: 2202-2207(2020)
- 作者机构：
  
  1. 中南大学计算机学院,湖南,长沙,410083
  2. 中国航空无线电电子研究所,上海,200241
  3. 中南大学软件学院,湖南,长沙,410083
  4. 中南大学计算机学院,湖南,长沙,410083
  5. 中国航空无线电电子研究所,上海,200241
  6. 中南大学软件学院,湖南,长沙,410083
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.11.015
  CLC： TP301
- Published Online：25 November 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
LIAO Zhi-fang, YANG Hong-yu, SONG Tian-hui, et al. Developer Project Recommendation Model Based on CNN-LSTM in GitHub[J]. Acta Electronica Sinica, 2020, 48(11): 2202-2207.
DOI：

LIAO Zhi-fang, YANG Hong-yu, SONG Tian-hui, et al. Developer Project Recommendation Model Based on CNN-LSTM in GitHub[J]. Acta Electronica Sinica, 2020, 48(11): 2202-2207. DOI： 10.3969/j.issn.0372-2112.2020.11.015.

摘要

作为一个开源项目托管平台，GitHub以多开发者协同参与进行开源项目的开发，开发者作为GitHub的核心元素，保证了整个系统的活跃性，然而，很多新项目在短时间内无法找到合适的协同开发者而被拖延开发周期.针对这个问题，本文提出了一种基于Word2Vec的CNN-LSTM开发者项目推荐模型，该模型以Word2Vec训练开发者访问项目的序列，并将项目进行向量化表示，结合CNN-LSTM模型计算项目相似度并为开发者推荐合适的项目序列.通过提取GitHub中62，031个开发者在2015全年的项目访问数据进行项目预测和相似项目发现实验，实验结果表明，该模型推荐效果较佳，并且可以帮助开发者发现感兴趣的相似项目.

Abstract

As an open source project hosting platform

GitHub participates in the development of open source projects with multi-developers. As the core element of GitHub

developers ensure the activity of the whole system. However

many new projects can not find suitable collaborative developers in a short time and the development cycle gets delayed. To solve this problem

this paper proposes a CNN-LSTM developer project recommendation model based on Word2Vec

which trains developers to access the project sequence by Word2Vec

and vectorizes the project

calculates the project similarity with CNN-LSTM model

and recommends the appropriate project sequence for developers. Through the project prediction and similar project discovery experiments based on 62

031 developers' project access data in GitHub in 2015

the experimental results show that the model has better recommendation effect and can help developers find similar projects of interest.

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