mlsanityData to 7 Oct 2026

Code Recommendation for Open Source Software Developers

arXiv 2210.08332 ↗First posted 15 Oct 2022cs.SE cs.IR cs.LGcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 44 of its 89 references that are in the map. First posted on arXiv 15 Oct 2022.

Builds on

44 of 89 references.

References · 44

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32 more
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Citing papers per year: 2022: 1; 2023: 5; 2024: 5; 2025: 2.

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  2. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  3. 2023Continual Learning on Dynamic Graphs via Parameter Isolationcited by 35
  4. 2023TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systemscited by 11
  5. 2022Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizescited by 10
  6. 2025Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generationcited by 9
  7. 2024GPT4Rec: Graph Prompt Tuning for Streaming Recommendationcited by 7
  8. 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
  9. 2023arXiv4TGC: Large-Scale Datasets for Temporal Graph Clusteringcited by 3
  10. 2024Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectoriescited by 3
  11. 2024Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domainscited by 1
  12. 2024Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)cited by 0
1 more
  1. 2025The Influence of Text Variation on User Engagement in Cross-Platform Content Sharingcited by 0