Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Linyxus/awesome-neural-code-intelligence

A curated list for awesome machine learning methods for neural code intelligence.
https://github.com/Linyxus/awesome-neural-code-intelligence

List: awesome-neural-code-intelligence

Last synced: 16 days ago
JSON representation

A curated list for awesome machine learning methods for neural code intelligence.

Host: GitHub
URL: https://github.com/Linyxus/awesome-neural-code-intelligence
Owner: Linyxus
Created: 2021-11-08T11:10:38.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2021-11-26T10:59:34.000Z (about 3 years ago)
Last Synced: 2024-04-11T08:01:36.289Z (8 months ago)
Size: 6.84 KB
Stars: 6
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - awesome-neural-code-intelligence - A curated list for awesome machine learning methods for neural code intelligence. (Other Lists / Monkey C Lists)

README

        # Awesome Neural Code Intelligence

A curated list for awesome machine learning methods for neural code intelligence.

## Websites

- [ML for Code](https://ml4code.github.io)

## Paper List

### RNN/LSTM-based

- code2vec: Learning Distributed Representations of Code, Alon et al., Proc. ACM Program. Lang. (2019): 40:1-40:29

  [[arXiv]](https://arxiv.org/abs/1803.09473)

  [[GitHub]](https://github.com/tech-srl/code2vec)

  [[Demo]](https://code2vec.org/)

- code2seq: Generating Sequences from Structured Representations of Code, Alon et al., ICLR (2019)

  [[arXiv]](https://arxiv.org/abs/1808.01400)

  [[GitHub]](https://github.com/tech-srl/code2seq)

  [[Demo]](https://code2seq.org/)

### Transformer-based

- CodeBERT: A Pre-Trained Model for Programming and Natural Languages, Feng et al., EMNLP (2020): 1536-1547 

  [[arXiv]](https://arxiv.org/abs/2002.08155)

  [[GitHub]](https://github.com/microsoft/CodeBERT)

- GraphCodeBERT: Pre-training Code Representations with Data Flow, Guo et al., ICLR (2021)

  [[OpenReview]](https://openreview.net/pdf?id=jLoC4ez43PZ)

  [[GitHub]](https://github.com/microsoft/CodeBERT)

- CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation., Wang et al., arXiv (2021) 

  [[arXiv]](https://arxiv.org/abs/2109.00859) 

  [[GitHub]](https://github.com/salesforce/CodeT5)

- PyMT5: multi-mode translation of natural language and Python code with transformers, Clement et al., EMNLP (2020): 9052-9065

  [[arXiv]](https://arxiv.org/abs/2010.03150)

- Evaluating Large Language Models Trained on Code, Chen et al., CoRR (2021)

  [[arXiv]](https://arxiv.org/abs/2107.03374) 

  [[GitHub]](https://github.com/openai/human-eval)

- Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks, Mastropaolo et al., ICSE (2021): 336-347

  [[arXiv]](https://arxiv.org/abs/2102.02017)

  [[GitHub]](https://github.com/antonio-mastropaolo/T5-learning-ICSE_2021)

- Multi-task Learning based Pre-trained Language Model for Code Completion, Liu et al., ASE (2020): 473-485

  [[arXiv]](https://arxiv.org/abs/2012.14631)

  [[GitHub]](https://github.com/LiuFang816/CugLM)

- Unsupervised Translation of Programming Languages, Rozière et al., NeurIPS (2020)

  [[arXiv]](https://arxiv.org/abs/2006.03511) 

  [[GitHub]](https://github.com/facebookresearch/CodeGen)

- DOBF: A Deobfuscation Pre-Training Objective for Programming Languages, Rozière et al., CoRR (2021)

  [[arXiv]](https://arxiv.org/abs/2102.07492)

  [[GitHub]](https://github.com/facebookresearch/CodeGen)

- Leveraging Automated Unit Tests for Unsupervised Code Translation, Rozière et al., CoRR (2021)

  [[arXiv]](https://arxiv.org/abs/2110.06773) 

  [[GitHub]](https://github.com/facebookresearch/CodeGen)

- IntelliCode compose: code generation using transformer, Svyatkovskiy et al., ESEC/SIGSOFT FSE (2020): 1433-1443

  [[arXiv]](https://arxiv.org/abs/2005.08025)

- Exploring Software Naturalness through Neural Language Models, Buratti et al., CoRR (2020)

  [[arXiv]](https://arxiv.org/abs/2006.12641)

- Unified Pre-training for Program Understanding and Generation, Ahmad et al., NAACL-HLT (2021): 2655-2668

  [[arXiv]](https://arxiv.org/abs/2103.06333)

  

### GNN-based

- Learning Execution through Neural Code Fusion, Shi et al., ICLR (2020)

  [[arXiv]](https://arxiv.org/abs/1906.07181)

  [[Talk]](https://papertalk.org/papertalks/3759)

- Learning to Represent Programs with Graphs, Allamanis et al., ICLR (2018)

  [[arXiv]](https://arxiv.org/abs/1711.00740)

  [[GitHub]](https://github.com/Microsoft/graph-based-code-modelling)

  

### Benchmarks & Surveys

- CodeBLEU: a Method for Automatic Evaluation of Code Synthesis, Ren et al., CoRR (2020)

  [[arXiv]](https://arxiv.org/abs/2009.10297)

- CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation, Lu et al., CoRR (2021)

  [[arXiv]](https://arxiv.org/abs/2102.04664)

  [[GitHub]](https://github.com/microsoft/CodeXGLUE)

  [[Website]](https://microsoft.github.io/CodeXGLUE/)

- Measuring Coding Challenge Competence With APPS, Hendrycks et al., CoRR (2021)

  [[arXiv]](https://arxiv.org/abs/2105.09938)