# Code Recommendation for Open Source Software Developers arXiv: https://arxiv.org/abs/2210.08332 ยท HTML: https://mlsanity.com/paper/2210.08332/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 44 of its 89 references that are in the map. - First posted on arXiv 15 Oct 2022. ## Builds on (most cited first, 30 of 44) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018) - [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020) - [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018) - [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017) - [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019) - [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019) - [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019) - [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020) - [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020) - [Deep Modular Co-Attention Networks for Visual Question Answering](https://mlsanity.com/paper/1906.10770/) (2019) - [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018) - [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016) - [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018) - [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021) - [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://mlsanity.com/paper/2202.06200/) (2022) - [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021) - [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019) - [A parallel corpus of Python functions and documentation strings for automated code documentation and code generation](https://mlsanity.com/paper/1707.02275/) (2017) - [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021) - [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021) - [Aroma: Code Recommendation via Structural Code Search](https://mlsanity.com/paper/1812.01158/) (2018) ## Built on by (most cited first, 13 of 13) - [Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries](https://mlsanity.com/paper/2310.13132/) (2023) - [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023) - [Continual Learning on Dynamic Graphs via Parameter Isolation](https://mlsanity.com/paper/2305.13825/) (2023) - [TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2308.14355/) (2023) - [Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes](https://mlsanity.com/paper/2211.13638/) (2022) - [Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generation](https://mlsanity.com/paper/2502.03233/) (2025) - [GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://mlsanity.com/paper/2406.08229/) (2024) - [Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation](https://mlsanity.com/paper/2402.15988/) (2024) - [arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering](https://mlsanity.com/paper/2306.04962/) (2023) - [Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories](https://mlsanity.com/paper/2406.17963/) (2024) - [Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains](https://mlsanity.com/paper/2401.13129/) (2024) - [Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)](https://mlsanity.com/paper/2409.16590/) (2024) - [The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing](https://mlsanity.com/paper/2505.03769/) (2025) ## Abstract (arXiv) 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.