Personalized Repository Recommendation Service for Developers with Multi-modal Features Learning

Yueshen Xu, Yuhong Jiang, Xinkui Zhao, Ying Li, Rui Li

Abstract: Nowadays an increasing number of software developers have joined in open-source software development communities such as GitHub, and develop and share softwares in these communities. Those online communities contain a huge volume of open-source repositories. Developers commonly search from existing repositories and intend to find suitable repositories to their development requirements. However, it is time-and energy-consuming to discover suitable repositories from such a large number of candidates and it may be also hard for developers to choose accurate keywords. So an effective repository recommendation service becomes an indispensable tool for developers. There have been some solutions for repository recommendation, but existing solutions have several defects such as mediocre accuracy and ignorance of useful features. In this paper, we develop a new personalized repository recommendation service with multi-modal features learning. We propose to mine two modes of features and jointly utilize the mined multimodal features. One of the features is the developers’ sequential behavior features and the other is text features of repositories. We design novel features learning mechanisms for the two modes of features. We performed sufficient experiments on a real-world dataset and the experimental results demonstrate that our model generates superior recommendation results and produces an improvement of 15.3% and 14.5% in Precision and Recall compared to well-known existing methods.