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。

开源软件开发者的代码推荐

Code Recommendation for Open Source Software Developers

Programmers often write code that has similarity to existing code written
somewhere. A tool that could help programmers to search such similar code would
be immensely useful. Such a tool could help programmers to extend partially
written code snippets to completely implement necessary functionality, help to
discover extensions to the partial code which are commonly included by other
programmers, help to cross-check against similar code written by other
programmers, or help to add extra code which would fix common mistakes and
errors. We propose Aroma, a tool and technique for code recommendation via
structural code search. Aroma indexes a huge code corpus including thousands of
open-source projects, takes a partial code snippet as input, searches the
corpus for method bodies containing the partial code snippet, and clusters and
intersects the results of the search to recommend a small set of succinct code
snippets which both contain the query snippet and appear as part of several
methods in the corpus. We evaluated Aroma on 2000 randomly selected queries
created from the corpus, as well as 64 queries derived from code snippets
obtained from Stack Overflow, a popular website for discussing code. We
implemented Aroma for 4 different languages, and developed an IDE plugin for
Aroma. Furthermore, we conducted a study where we asked 12 programmers to
complete programming tasks using Aroma, and collected their feedback. Our
results indicate that Aroma is capable of retrieving and recommending relevant
code snippets efficiently.

Aroma 是一种通过结构代码搜索实现代码推荐的工具和技术，能够检索并推荐相关的代码片段，可以有效地帮助程序员完成编程任务。