{"id":27713,"url":"https://github.com/Linyxus/awesome-neural-code-intelligence","name":"awesome-neural-code-intelligence","description":"A curated list for awesome machine learning methods for neural code intelligence.","projects_count":38,"last_synced_at":"2026-06-04T15:00:20.871Z","repository":{"id":94199606,"uuid":"425805465","full_name":"Linyxus/awesome-neural-code-intelligence","owner":"Linyxus","description":"A curated list for awesome machine learning methods for neural code intelligence.","archived":false,"fork":false,"pushed_at":"2021-11-26T10:59:34.000Z","size":7,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-05-02T13:03:34.789Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Linyxus.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-11-08T11:10:38.000Z","updated_at":"2025-07-08T09:57:33.000Z","dependencies_parsed_at":"2023-06-19T09:45:15.555Z","dependency_job_id":null,"html_url":"https://github.com/Linyxus/awesome-neural-code-intelligence","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Linyxus/awesome-neural-code-intelligence","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Linyxus%2Fawesome-neural-code-intelligence","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Linyxus%2Fawesome-neural-code-intelligence/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Linyxus%2Fawesome-neural-code-intelligence/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Linyxus%2Fawesome-neural-code-intelligence/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Linyxus","download_url":"https://codeload.github.com/Linyxus/awesome-neural-code-intelligence/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Linyxus%2Fawesome-neural-code-intelligence/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33910137,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-04T02:00:06.755Z","response_time":64,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T12:57:32.769Z","updated_at":"2026-06-04T15:00:20.872Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Paper List","Websites"],"sub_categories":["RNN/LSTM-based","Transformer-based","Benchmarks \u0026 Surveys","GNN-based"],"readme":"# Awesome Neural Code Intelligence\nA curated list for awesome machine learning methods for neural code intelligence.\n\n## Websites\n\n- [ML for Code](https://ml4code.github.io)\n\n## Paper List\n\n### RNN/LSTM-based\n\n- code2vec: Learning Distributed Representations of Code, Alon et al., Proc. ACM Program. Lang. (2019): 40:1-40:29\n  [[arXiv]](https://arxiv.org/abs/1803.09473)\n  [[GitHub]](https://github.com/tech-srl/code2vec)\n  [[Demo]](https://code2vec.org/)\n- code2seq: Generating Sequences from Structured Representations of Code, Alon et al., ICLR (2019)\n  [[arXiv]](https://arxiv.org/abs/1808.01400)\n  [[GitHub]](https://github.com/tech-srl/code2seq)\n  [[Demo]](https://code2seq.org/)\n\n### Transformer-based\n\n- CodeBERT: A Pre-Trained Model for Programming and Natural Languages, Feng et al., EMNLP (2020): 1536-1547 \n  [[arXiv]](https://arxiv.org/abs/2002.08155)\n  [[GitHub]](https://github.com/microsoft/CodeBERT)\n- GraphCodeBERT: Pre-training Code Representations with Data Flow, Guo et al., ICLR (2021)\n  [[OpenReview]](https://openreview.net/pdf?id=jLoC4ez43PZ)\n  [[GitHub]](https://github.com/microsoft/CodeBERT)\n- CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation., Wang et al., arXiv (2021) \n  [[arXiv]](https://arxiv.org/abs/2109.00859) \n  [[GitHub]](https://github.com/salesforce/CodeT5)\n- PyMT5: multi-mode translation of natural language and Python code with transformers, Clement et al., EMNLP (2020): 9052-9065\n  [[arXiv]](https://arxiv.org/abs/2010.03150)\n- Evaluating Large Language Models Trained on Code, Chen et al., CoRR (2021)\n  [[arXiv]](https://arxiv.org/abs/2107.03374) \n  [[GitHub]](https://github.com/openai/human-eval)\n- Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks, Mastropaolo et al., ICSE (2021): 336-347\n  [[arXiv]](https://arxiv.org/abs/2102.02017)\n  [[GitHub]](https://github.com/antonio-mastropaolo/T5-learning-ICSE_2021)\n- Multi-task Learning based Pre-trained Language Model for Code Completion, Liu et al., ASE (2020): 473-485\n  [[arXiv]](https://arxiv.org/abs/2012.14631)\n  [[GitHub]](https://github.com/LiuFang816/CugLM)\n- Unsupervised Translation of Programming Languages, Rozière et al., NeurIPS (2020)\n  [[arXiv]](https://arxiv.org/abs/2006.03511) \n  [[GitHub]](https://github.com/facebookresearch/CodeGen)\n- DOBF: A Deobfuscation Pre-Training Objective for Programming Languages, Rozière et al., CoRR (2021)\n  [[arXiv]](https://arxiv.org/abs/2102.07492)\n  [[GitHub]](https://github.com/facebookresearch/CodeGen)\n- Leveraging Automated Unit Tests for Unsupervised Code Translation, Rozière et al., CoRR (2021)\n  [[arXiv]](https://arxiv.org/abs/2110.06773) \n  [[GitHub]](https://github.com/facebookresearch/CodeGen)\n- IntelliCode compose: code generation using transformer, Svyatkovskiy et al., ESEC/SIGSOFT FSE (2020): 1433-1443\n  [[arXiv]](https://arxiv.org/abs/2005.08025)\n- Exploring Software Naturalness through Neural Language Models, Buratti et al., CoRR (2020)\n  [[arXiv]](https://arxiv.org/abs/2006.12641)\n- Unified Pre-training for Program Understanding and Generation, Ahmad et al., NAACL-HLT (2021): 2655-2668\n  [[arXiv]](https://arxiv.org/abs/2103.06333)\n  \n### GNN-based\n\n- Learning Execution through Neural Code Fusion, Shi et al., ICLR (2020)\n  [[arXiv]](https://arxiv.org/abs/1906.07181)\n  [[Talk]](https://papertalk.org/papertalks/3759)\n- Learning to Represent Programs with Graphs, Allamanis et al., ICLR (2018)\n  [[arXiv]](https://arxiv.org/abs/1711.00740)\n  [[GitHub]](https://github.com/Microsoft/graph-based-code-modelling)\n  \n### Benchmarks \u0026 Surveys\n\n- CodeBLEU: a Method for Automatic Evaluation of Code Synthesis, Ren et al., CoRR (2020)\n  [[arXiv]](https://arxiv.org/abs/2009.10297)\n- CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation, Lu et al., CoRR (2021)\n  [[arXiv]](https://arxiv.org/abs/2102.04664)\n  [[GitHub]](https://github.com/microsoft/CodeXGLUE)\n  [[Website]](https://microsoft.github.io/CodeXGLUE/)\n- Measuring Coding Challenge Competence With APPS, Hendrycks et al., CoRR (2021)\n  [[arXiv]](https://arxiv.org/abs/2105.09938)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/linyxus%2Fawesome-neural-code-intelligence/projects"}