Open Source Software (OSS) is forming the spines of technology
infrastructures, attracting millions of talents to contribute. Notably, it is
challenging and critical to consider both the developers' interests and the
semantic features of the project code to recommend appropriate development
tasks to OSS developers. In this paper, we formulate the novel problem of code
recommendation, whose purpose is to predict the future contribution behaviors
of developers given their interaction history, the semantic features of source
code, and the hierarchical file structures of projects. Considering the complex
interactions among multiple parties within the system, we propose CODER, a
novel graph-based code recommendation framework for open source software
developers. CODER jointly models microscopic user-code interactions and
macroscopic user-project interactions via a heterogeneous graph and further
bridges the two levels of information through aggregation on file-structure
graphs that reflect the project hierarchy. Moreover, due to the lack of
reliable benchmarks, we construct three large-scale datasets to facilitate
future research in this direction. Extensive experiments show that our CODER
framework achieves superior performance under various experimental settings,
including intra-project, cross-project, and cold-start recommendation. We will
release all the datasets, code, and utilities for data retrieval upon the
acceptance of this work.

本文提出了一种新的代码推荐框架 CODER，通过建立异构图并对文件结构进行聚合，实现了微观用户－代码交互和宏观用户 - 项目交互的联合建模，进而预测开源软件开发者的未来贡献行为。该框架在多种实验设置下（包括项目内、跨项目和冷启动推荐）都取得了优异的性能表现，作者还构建了三个大规模的数据集，以促进未来的研究方向。