Awesome-Code-LLM
[TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets.
https://github.com/codefuse-ai/Awesome-Code-LLM
Last synced: 9 days ago
JSON representation
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9. Recommended Readings
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8.2 Benchmarks
- Attention Is All You Need - attention for long-range dependency and parallel training |
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher
- Neural Machine Translation by Jointly Learning to Align and Translate - decoder RNN |
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Language Models are Few-Shot Learners - 2 (175B), they discovered a new learning paradigm: In-Context Learning (ICL) |
- Training language models to follow instructions with human feedback - 3 instruction finetuned with RLHF (reinforcement learning from human feedback) |
- Multitask Prompted Training Enables Zero-Shot Task Generalization
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models - of-Though reasoning |
- Language Models are Unsupervised Multitask Learners
- Improving Language Understanding by Generative Pre-Training - finetuning paradigm applied to Transformer decoder |
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer - decoder pretrained with an MLM-like denoising objective |
- LoRA: Low-Rank Adaptation of Large Language Models - efficient finetuning |
- PaLM: Scaling Language Modeling with Pathways
- LLaMA - 4](https://arxiv.org/abs/2303.08774) or [PaLM 2](https://arxiv.org/abs/2305.10403). For comprehensive reviews on these more general topics, we refer to other sources such as [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM), [Awesome AIGC Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials), or for LLM applications in other specific domains: [Awesome Domain LLM](https://github.com/luban-agi/Awesome-Domain-LLM), [Awesome Tool Learning](https://github.com/luban-agi/Awesome-Tool-Learning#awesome-tool-learning), [Awesome-LLM-MT](https://github.com/hsing-wang/Awesome-LLM-MT), [Awesome Education LLM](https://github.com/Geralt-Targaryen/Awesome-Education-LLM).
- RoFormer: Enhanced Transformer with Rotary Position Embedding
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model - source dense LLM, trained on 46 languages, with detailed discussion about training and evaluation |
- The Pile: An 800GB Dataset of Diverse Text for Language Modeling
- Neural Machine Translation of Rare Words with Subword Units - pair encoding: split rare words into subword units |
- Mixed Precision Training
- GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models - efficient distributed optimization |
- Measuring Massive Multitask Language Understanding - knowledge and complex reasoning benchmark |
- Finetuned Language Models Are Zero-Shot Learners - finetuning |
- Training Compute-Optimal Large Language Models
- Large Language Models are Zero-Shot Reasoners
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models - knowledge and complex reasoning benchmark |
- Emergent Abilities of Large Language Models
- Scaling Instruction-Finetuned Language Models
- Self-Instruct: Aligning Language Models with Self-Generated Instructions - generated data |
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2. Models
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2.1 Base LLMs and Pretraining Strategies
- 2023-03
- 2023-12
- 2023-09 - 1_5)]
- 2023-07
- 2022-01
- 2022-04 - neox)]
- 2023-09 - inc/Baichuan2)]
- 2023-09
- 2023-12
- 2023-10 - src)]
- 2023-12 - research/YAYI2)]
- 2024-01 - ai/DeepSeek-LLM)]
- 2024-01 - of-experts/)]
- 2024-01
- 2024-02
- 2024-02 - open-models/)]
- 2024-01 - ai/DeepSeek-MoE)]
- 2024-03 - ai/Yi)]
- 2024-03 - 3-family)]
- 2024-04 - 34B)]
- 2024-04 - ai/JetMoE)]
- 2024-04
- 2024-04 - llama/llama3)] [[paper](https://arxiv.org/abs/2407.21783)]
- 2024-05 - ai/DeepSeek-V2)]
- 2024-04
- 2024-04
- 2024-04 - FLM)]
- 2024-05 - 7B)]
- 2024-05 - art-projection/MAP-NEO)]
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 2024-09
- 2024-09
- 2024-06
- 2024-09
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-11
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2024-06
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-02
- 2025-03
- 2025-04
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-07
- 2025-07
- 2025-07
- 2025-08
- 2025-07
- 2025-08
- 2025-09
- 2025-09
- 2025-10
- 2025-11
- 2025-12
- 2026-01
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2.3 General Pretraining on Code
- 2020-02
- 2023-06 - 1)]
- 2023-05
- 2022-03
- 2023-05
- 2022-02 - LMs)]
- 2019-12 - research/google-research/tree/master/cubert)]
- 2020-09
- 2021-08
- 2021-10
- 2022-05
- 2020-05
- 2021-12
- 2022-04
- 2022-06
- 2022-07
- 2023-01
- 2020-10
- 2021-02 - mastropaolo/TransferLearning4Code)]
- 2021-02
- 2021-03
- 2021-09
- 2022-01
- 2022-06
- 2020-12
- 2022-03
- 2022-12 - code)]
- 2024-01 - ai/DeepSeek-Coder)]
- 2024-02
- 2024-01
- 2024-03
- 2024-04
- 2024-05 - 07] [[paper](https://arxiv.org/abs/2407.13739)]
- 2024-02
- 2024-07
- 2024-09
- 2024-10
- 2024-11
- 2025-01
- 2025-03
- 2025-05
- 2025-06
- 2025-05
- 2025-06
- 2025-09
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-10
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2.2 Existing LLM Adapted to Code
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2.4 (Instruction) Fine-Tuning on Code
- 2023-07
- 2023-06
- 2023-11 - ai/MFTCoder)]
- 2023-12
- 2023-12
- 2024-01
- 2024-03
- 2024-04
- 2024-04
- 2024-04 - uiuc/xft)]
- 2024-05
- 2023-09
- 2023-11
- 2024-05
- 2024-05
- 2024-05
- 2024-02
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-07
- [paper
- 2024-02
- 2024-07
- 2024-08
- ACL 2024 Findings
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-12
- 2025-01
- 2024-06
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-04
- 2025-04
- 2025-04
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-06
- 2025-07
- 2025-07
- 2025-07
- 2025-09
- 2025-09
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-12
- 2026-01
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2.5 Reinforcement Learning on Code
- 2022-07
- 2023-10
- 2022-03
- 2023-01 - lab-code-research/PPOCoder)]
- 2023-07 - scut/RLTF)]
- 2024-02
- 2024-04
- 2024-06
- 2024-06
- 2024-09
- 2024-01
- 2024-10
- 2024-10
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-05
- 2025-06
- 2025-06
- 2025-05
- 2025-06
- 2025-08
- 2025-08
- 2025-09
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-10
- 2025-11
- 2026-01
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8. Datasets
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8.2 Benchmarks
- [paper
- [paper
- [paper - codes/VulDeePecker)] |
- [paper
- [paper - eval)] |
- [paper
- [paper - deepmind/code_contests)] |
- [paper
- [paper - research/google-research/tree/master/mbpp)] [[MathQA-Python](https://github.com/google/trax/blob/master/trax/examples/MathQA_Python_generation_notebook.ipynb)] |
- [paper - ai/DS-1000)] |
- [paper
- 2023-10 - ai/codefuse-evaluation)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - conala)] |
- [paper - plugin/nl2code-dataset)] |
- [paper - E)] |
- [paper - science/mxeval)] |
- [paper - jie-Huang/ExeDS)] |
- [paper
- [paper
- [paper
- [paper
- [paper - easel/StudentEval)] |
- [paper
- [paper
- [paper - eval)] |
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper
- [paper
- [paper - data/)] |
- [paper - lily.github.io/spider)] |
- [paper - lily.github.io/sparc)] |
- [paper
- [paper - lily.github.io/cosql)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - DK)] |
- [paper - SpiderCG)] |
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper - lab-code-research/MuST-CoST)] |
- [paper - lab-code-research/XLCoST)] |
- [paper - TransEval)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - fixes)] |
- [paper - bugs/bears-benchmark)] |
- [paper - codechanges)] |
- [paper
- [paper
- [paper - group/mineSStuBs)] |
- [paper
- [paper - sri/TFix)] |
- [paper - KTH/megadiff)] |
- [paper
- [paper
- [paper
- [paper
- [paper - docstring-corpus)] |
- [paper - hu/TL-CodeSum)] |
- [paper
- [paper
- [paper - kb/tree/main/MSR2019)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - us/download/103554)] |
- [paper
- [paper - group/diversevul)] |
- [paper
- [paper - EA6F/)] |
- [paper
- [paper - Question-Code-Dataset)] |
- [paper - corpus.github.io/)] |
- [paper - Code-Search-Evaluation-Dataset)] |
- [paper - LAB-SJTU/CosBench/wiki)] |
- [paper
- [paper
- [paper - Code/NL-code-search-WebQuery)] |
- [paper - lab.org/projects/TypeWriter/data.tar.gz)] |
- [paper
- [paper
- [paper - types-4-py-dataset)] |
- [paper
- [paper - group/TypeT5)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - group/CoDiSum)] |
- [paper
- [paper - autosuggestions)] |
- [paper
- [paper - Research/commit_message_generation)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2020-09
- [paper
- 2024-03
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2024-04
- [paper
- [paper - nlp/USACO)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Targaryen/MC-Evaluation)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - 810A)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Lab/BookSQL)] |
- [paper
- [paper
- [paper
- 2024-06 - rag-bench/code-rag-bench)]
- [paper - bench/JavaBench)] |
- 2024-06 - Research/lca-baselines)]
- [paper
- [paper
- [paper
- [paper
- 2024-06
- [paper - project/bigcodebench)] |
- [paper
- [paper - AI/RES-Q)] |
- [paper - team/coir)] |
- [paper
- [paper
- [paper - liuzy/CodeUpdateArena)] |
- [paper
- [paper - NLP/novicode)] |
- [paper
- 2024-02
- [paper
- [paper - bench/SciCode)] |
- [paper
- [paper
- [paper - ai/WebApp1K-React)] |
- [paper
- [paper
- [paper
- 2023-11
- 2024-08
- [paper
- [paper - eval)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Research/CodeJudge-Eval)] |
- [paper
- [paper
- [paper - X/cruxeval-x)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - swe-bench/multi-swe-bench.github.io)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - jie-Huang/CoCoNote)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- 2024-10
- [paper - ai/geospatial-code-llms-dataset)] |
- [paper - AI4Code/CodeMMLU)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Bench)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - code)] |
- [paper - V/HumanEval-V-Benchmark)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - gmu/mHumanEval)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - Eval-Team/M2RC-Eval)] |
- [paper
- [paper - nl2sql)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - AES-AI4Code/CodeQuestionAnswering)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - xl)] |
- [paper
- [paper - ai/Spider2)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper - tan/CoCoNut-Artifact)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - Eval)] |
- [paper
- [paper - benchmarks/tree/main/MBUPP)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - li/CleanVul)] |
- [paper - 0/commit0)] |
- 2024-11
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2024-12 - eval)]
- [paper - evolution-eval.github.io/)] |
- [paper - Research/plot_bench)] |
- [paper - Coder/tree/main/qwencoder-eval/instruct/CodeArena)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Coder/tree/main/qwencoder-eval/instruct/CodeArena)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - bench.github.io/)] |
- [paper - Pro/CodeEval-Pro/tree/main)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - code-search)] |
- [paper - Bench)] |
- 2025-01
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - hu/DeepCom)] |
- 2025-01
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - benchmark)] |
- 2025-02
- [paper - dougherty/fvapps)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-02
- 2025-02
- 2023-02
- 2025-02
- 2025-02
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Benchmark)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-02 - deepmind/bbeh)]
- 2025-02
- 2025-02
- 2025-02
- [paper - Bench-D65E/README.md)] |
- [paper - XL)] |
- [paper - 7B74/README.md)] |
- [paper - 9/probench)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - sudo/DependEval)] |
- [paper
- [paper - dot-jar/bugs-dot-jar)] |
- [paper
- [paper - USZ/FixJS)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Benchmark/Tests-C250)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - science/SWE-PolyBench)] |
- [paper - swe-bench/multi-swe-bench)] |
- 2025-04
- [paper - level-benchmark-dataset-B132/README.md)] |
- [paper
- [paper - bench)] |
- [paper - level-Vulnerability-Detection)] |
- 2025-04
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - codegen/yabloco-benchmark)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - bench.github.io)] |
- 2025-06
- 2025-05
- 2025-05
- 2025-05
- [paper - bench/oss-bench)] |
- [paper
- [paper
- [paper - github/Flow2Code)] |
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper - CARD/biomedsql)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - TrustEval-C)] |
- [paper - Dev)] |
- [paper - rebench)] |
- [paper - Bench)] |
- [paper - Research/git-good-bench)] |
- [paper - Pro)] |
- [paper - s-Last-Code-Exam/HLCE)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - Ren/OJBench)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-06
- [paper - Hunyuan/ArtifactsBenchmark)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Eval-Official/CoreCodeBench)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-07 - Evaluation/MERA_CODE)]
- 2025-07
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- 2025-07 - perf/swe-perf)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - bench_Pro-os)] |
- [paper - weihan/SWE-QA-Bench)] |
- 2025-09
- [paper - a-p/AetherCode)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- 2025-10
- 2025-10
- [paper
- [paper - interact.github.io/)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-10
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- 2025-10
- [paper
- [paper - ai/Falcon)] |
- [paper - level-Vulnerability-Detection)] |
- [paper - JPG/VCode)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - Replication)] |
- [paper - level-Vulnerability-Detection)] |
- [paper - Sharp-Bench)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - Computing-Lab/gpuFLOPBench)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- 2025-12
- [paper
- [paper - EVO)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- 2026-01
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
-
8.1 Pretraining
-
-
3. When Coding Meets Reasoning
-
3.3 Code Agents
- 2023-07
- 2023-08
- 2023-04
- 2024-03
- 2024-03
- 2024-03
- 2024-03
- 2024-04
- 2024-04
- 2024-05
- 2023-10
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-01
- 2024-09
- 2024-08
- 2024-09 - websoft/PairCoder)]
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-08
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-04
- 2025-04
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-06
- 2025-06
- 2025-05
- 2025-06
- 2025-06
- 2025-06
- 2025-07
- 2025-07
- 2025-07
- 2025-08
- 2025-09
- 2025-09
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-10
- 2025-10
- 2025-10
- 2025-11
- 2025-11
- 2025-11
- 2025-11
- 2025-11
- 2025-12
- 2025-12
- 2025-12
- 2025-12
- 2025-12
- 2025-12
- 2025-12
- 2026-01
- 2026-01
- 2026-01
-
3.4 Interactive Coding
- 2023-03
- 2023-04
- 2023-06
- 2023-03
- 2023-06
- 2023-05
- 2017-03
- 2022-08
- 2023-05
- 2024-02
- 2024-03
- 2024-03
- 2024-04
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-07
- 2023-11
- 2024-08
- 2024-09
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2020-06
- 2024-12
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-06
- 2025-07
- 2025-02
- 2025-09
- 2025-10
-
3.5 Frontend Navigation
- 2021-12
- 2023-07
- 2023-07
- 2023-06
- 2022-07
- 2021-10
- 2022-01
- 2022-01
- 2022-02
- 2022-02
- 2022-10
- 2022-10
- 2023-01
- 2023-12
- 2024-01
- 2024-01
- 2024-02
- 2024-02
- 2024-04
- 2024-04
- 2024-04
- 2024-06
- 2021-10
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-03
- 2025-06
- 2025-08
- 2025-09
-
3.1 Coding for Reasoning
- 2023-05
- 2022-11 - machines/pal)]
- 2022-11 - of-Thoughts)]
- 2023-08
- 2023-12
- 2024-01
- 2024-04
- 2024-03
- 2024-05
- 2023-10
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-07
- 2024-07
- 2024-02
- 2024-01
- 2024-07
- 2024-07
- 2024-07
- 2024-02
- 2024-03
- 2024-01
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-01
- 2024-02
- 2024-02
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-06
- 2025-06
- 2025-09
- 2025-10
-
3.2 Code Simulation
-
-
5. Methods/Models for Downstream Tasks
-
Program Proof
-
Code Ranking
-
Binary Analysis and Decompilation
-
Program Repair
- 2021-02
- 2023-02
- 2024-04
- 2021-05
- 2021-06
- 2022-05
- 2022-07
- 2022-08
- 2022-10
- 2023-01
- 2023-03
- 2023-04
- 2023-04
- 2023-06
- 2024-04
- 2024-04
- 2022-11
- 2023-12
- 2024-04
- 2024-04
- 2024-05
- 2024-04
- 2024-04
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-07
- 2024-07
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-05
- 2025-06
- 2025-06
- 2025-06
- 2025-07
- 2025-05
- 2025-05
- 2025-07
- 2025-07
- 2025-08
- 2025-08
- 2025-10
- 2025-11
-
Frontend Development
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2024-03
- 2024-03
- 2024-04
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- [paper
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2025-02
- 2025-03
- 2025-05
- 2025-05
- 2025-06
- 2025-10
- 2025-10
- 2025-11
- 2025-11
- 2025-08
- 2025-12
- 2026-01
-
Code Translation
- 2021-10
- 2018-02
- 2024-03
- 2018-07
- 2022-06
- 2022-07
- 2023-02
- 2023-06
- 2023-08
- 2023-11
- 2023-10
- 2024-04
- 2024-04
- 2024-05
- 2024-05
- 2024-06
- 2024-07
- 2024-07
- 2024-08
- 2024-08
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-03
- 2025-03
- 2025-04
- 2025-05
- 2025-05
- 2025-05
- 2025-07
- 2025-10
- 2025-11
- 2026-01
-
Vulnerability Detection
- 2021-06
- 2018-04
- 2024-03
- 2020-01
- 2021-05
- 2021-10
- 2022-01
- 222-04
- 2022-05
- 2022-05
- 2022-09
- 2022-12
- [paper
- 2023-05
- 2023-06
- 2023-08
- 2023-08
- 2023-10
- 2023-11
- 2023-12
- 2024-01
- 2024-01
- 2024-02
- 2024-03
- 2024-04
- 2024-04
- 2024-04
- 2024-04
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2019-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-02
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-06
- 2025-06
- 2025-03
- 2025-06
- 2025-07
- 2025-07
- 2025-09
- 2025-10
- 2025-10
- 2025-11
-
Code Commenting and Summarization
- 2020-05
- 2020-12
- 2021-04
- 2022-03
- 2023-03
- 2023-05
- 2023-08
- 2023-08
- 2024-04
- 2024-04
- 2024-04
- 2024-05
- 2024-05 - Mint/DocuMint)]
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2022-05
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-12
- 2024-10
- 2024-10
- 2024-12
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2025-01
- 2024-10
- 2025-01
- 2024-10
- 2024-10
- 2024-10
- 2025-01
- 2025-02
- 2024-10
- 2025-02
- 2025-02
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2025-02
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2025-04
- 2025-04
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2025-10
- 2025-10
-
Repository-Level Coding
- 2022-06
- 2024-03
- 2022-12
- 2023-05
- 2024-03
- 2023-12
- 2024-03
- 2024-05
- 2024-04
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-01
- 2024-06
- 2024-07
- 2024-09
- 2024-08 - philia/CoEdPilot)]
- 2024-10
- 2024-11
- 2024-12
- 2024-12
- 2025-02
- 2025-03
- 2025-03
- 2025-04
- 2025-05
- 2025-05
- 2025-05
- 2025-08
- 2025-09
- 2025-09
- 2025-09
- 2025-10
- 2025-11
- 2026-01
- 2026-01
- 2026-01
-
Compiler Optimization
-
Text-To-SQL
- 2024-03
- 2024-04
- 2021-09
- 2022-04
- 2022-09
- 2022-10
- 2022-10
- 2023-03
- 2023-04
- 2023-05
- 2023-05
- 2023-05
- 2023-07
- 2023-08
- 2024-04
- 2024-04
- 2024-04
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2023-12
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 2024-08
- 2024-02
- 2024-08
- 2024-08
- 2024-08
- 2024-02
- 2024-02
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-02
- 2024-02
- 2024-07
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-06
- 2024-02
- 2025-09
- 2025-09
- 2025-09
- 2025-09
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-10
- 2025-10
- 2025-10
- 2025-10
- 2025-10
- 2025-11
- 2025-11
- 2025-05
- 2025-05
- 2025-05
- 2025-11
- 2025-11
- 2025-11
- 2025-11
- 2025-11
- 2025-12
- 2026-01
- 2026-01
- 2026-01
- 2026-01
- 2026-01
-
Malicious Code Detection
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2023-03
- 2023-05
- 2023-08
- 2023-12
- 2023-12
- 2024-03
- [paper
- [paper
- [paper
- 2024-04
- [paper
- 2024-07
- [paper
- [paper
- [paper
- [paper
- [paper
- 2024-08
- [paper
- [paper
- [paper
- [paper
- 2024-09
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2023-08
- [paper
- 2025-03
- 2025-04
-
Type Prediction
-
Test Generation
- 2020-09
- 2023-02
- 2023-02
- 2023-02
- 2023-04
- 2023-05
- 2023-05
- 2023-07
- 2023-07
- 2023-08
- 2023-08
- 2023-10
- 2023-10
- 2024-03
- 2024-04
- 2024-04
- 2024-04
- 2024-04
- 2024-04
- 2024-05
- 2024-04
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2023-05
- 2025-02
- 2025-02
- 2023-10
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-05
- 2025-06
- 2025-06
- 2025-03
- 2025-07
- 2025-08
- 2025-09
- 2025-10
-
Oracle Generation
-
Code Generation
- 2024-04
- 2024-03
- 2024-04
- 2024-04
- 2024-04
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2023-11
- 2024-07
- 2024-07
- 2024-07
- 2023-09
- 2024-01
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-04
- 2025-04
- 2025-07
- 2025-07
- 2025-07
- 2025-08
- 2025-08
- 2025-07
- 2025-08
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-10
- 2025-05
- 2025-11
- 2025-12
- 2025-12
- 2026-01
-
Log Analysis
-
Code Similarity and Embedding (Clone Detection, Code Search)
-
Mutation Testing
-
Requirement Engineering
-
Automated Machine Learning
-
Code RAG
-
Commit Message Generation
-
Code Review
- 2022-01
- 2022-08
- 2023-02
- 2023-08
- 2024-04
- 2024-05
- 2024-06
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-11
- 2024-02
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-05
- 2025-05
- 2025-07
- 2025-09
- 2025-09
- 2025-10
- 2025-11
- 2025-11
- 2025-12
- 2026-01
- 2026-01
-
Issue Resolution
-
Software Modeling
-
Fuzz Testing
-
Software Configuration
-
Code Refactoring and Migration
-
Code QA & Reasoning
-
-
1. Surveys
-
7. Human-LLM Interaction
-
Others
- 2023-02
- 2024-04
- 2024-04
- 2024-04
- 2024-04
- 2022-06
- 2022-10
- 2023-02
- 2023-04
- 2023-08
- 2023-09
- 2023-09
- 2023-10
- 2024-04
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-04
- 2024-08
- 2024-07
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2022-04
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2024-12
- 2024-07
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-04
- 2025-05
- 2025-09
- 2025-10
- 2025-11
- 2025-12
- 2025-12
-
-
4. Datasets
-
4.2 Benchmarks
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
-
-
News
- 2024/09/06
- 2024/02
- 2024/03 - of-Experts (MoE).
- IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
- Evaluating Frontier Models for Dangerous Capabilities
- LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
- Exploring Language Model's Code Generation Ability with Auxiliary Functions
- CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences
- Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks
- Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
- Best Practices and Lessons Learned on Synthetic Data for Language Models
- MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
- Compression Represents Intelligence Linearly
- LoRA Learns Less and Forgets Less
- MarkLLM: An Open-Source Toolkit for LLM Watermarking
- 2024/06
- 2024/07
- codefuse-ai/MFTCoder
- The Llama 3 Herd of Models
- 2024/08
- 2024/09/14
- 2024/10/22
- codefuse-ai/CodeFuse-muAgent
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- codefuse-ai/GALLa
- codefuse-ai/CodeFuse-CGM
- codefuse-ai/RepoFuse
- codefuse-ai/EasyDeploy
- codefuse-ai/rodimus
- codefuse-ai/CodeFuse-CGE
- codefuse-ai/D2LLM
- codefuse-ai/CodeFuse-MFT-VLM
- Qwen2.5-Omni Technical Report
- F2LLM - ai/CodeFuse-Embeddings)] [[model & data](https://huggingface.co/collections/codefuse-ai/codefuse-embeddings-68d4b32da791bbba993f8d14)]
- SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
- CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization
- CodeClash: Benchmarking Goal-Oriented Software Engineering
- Instella: Fully Open Language Models with Stellar Performance
- DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models - AI.
- 2025/12/09
- LLaDA2.0: Scaling Up Diffusion Language Models to 100B
- T5Gemma 2: Seeing, Reading, and Understanding Longer
- Olmo 3
- Scaling Laws for Code: Every Programming Language Matters
- NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
- SimpleDevQA: Benchmarking Large Language Models on Development Knowledge QA - Sen University.
- C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling
- Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
- MiMo-V2-Flash Technical Report
- K-EXAONE Technical Report
- SWE-RM: Execution-free Feedback For Software Engineering Agents
- Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model
- Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
- X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests
-
6. Analysis of AI-Generated Code
-
Security and Vulnerabilities
- 2022-08
- 2024-03
- 2021-08
- 2022-04
- 2022-1
- 2024-04
- 2024-05
- 2023-02
- 2023-12
- 2024-04
- 2024-04
- 2024-04
- 2024-05
- 2024-06
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 2024-08
- 2024-08
- 2024-03
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-11
- 2024-10
- 2024-11
- 2024-12
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-05
- 2025-05
- 2025-06
- 2025-07
- 2025-06
- 2024-08
- 2025-08
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-12
- 2025-12
- 2026-01
-
Correctness
-
Others
- 2024-04
- 2024-04
- 2024-05
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-09
- 2024-11
- 2024-11
- 2024-11
- 2023-12
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-05
- 2025-05
- 2025-10
- 2025-12
- 2025-12
- 2026-01
- 2026-01
-
Robustness
-
Hallucination
-
Efficiency
-
AI-Generated Code Detection
-
Privacy
-
Bias
-
Contamination
-
API Usage
-
Interpretability
-
-
5. Datasets
-
5.2 Benchmarks
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
-
-
Star History
-
5.2 Benchmarks
- ![Star History Chart - history.com/#codefuse-ai/Awesome-Code-LLM&Date)
-
8.2 Benchmarks
- ![Star History Chart - history.com/#codefuse-ai/Awesome-Code-LLM&Date)
-
-
4. Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages
-
3.5 Frontend Navigation
- **Haskell**
- **Ruby**
- **Verilog**
- **Hansl**
- **Verilog**
- **Verilog**
- **Racket, OCaml, Lua, R, Julia**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Alloy**
- **Verilog**
- **R**
- **Kotlin, Swift, and Rust**
- **Verilog**
- **Bash**
- **OpenAPI**
- **Fortran, Julia, Matlab, R, Rust**
- **Verilog**
- 2024-06
- **Logo**
- **Ansible YAML, Bash**
- **Qiskit**
- **Perl, Golang, Swift**
- **Verilog**
- 2024-07
- **Json, XLM, YAML**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **RPA**
- **Verilog**
- **Verilog**
- **MaxMSP, Web Audio**
- **Verilog**
- **Verilog**
- **Bash**
- **Survey**
- **OCL**
- **Ansible-YAML**
- **Verilog**
- **Kotlin**
- **R**
- 2024-10
- **PLC**
- **Lua**
- 2024-10
- 2024-10
- **R, D, Racket, Bash**
- **SPICE**
- **IEC 61131-3 ST**
- **Verilog**
- **Verilog**
- **MUMPS, ALC**
- **Power Query M, OfficeScript, Excel formulas**
- **ST**
- **Verilog**
- **HPC**
- **UCLID5**
- **Verilog**
- **G**
- **Julia, Lua, R, Racket**
- **F***
- 2025-02
- **Alloy***
- **Solidity**
- **PennyLane**
- **Verilog**
- **Modelica**
- **Excel**
- 2025-04
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Chisel**
- **Lean**
- **Verilog**
- **Verilog**
- **Verilog**
- **LaTeX**
- **Triton**
- **CUDA**
- **SIMD intrinsics**
- **Triton**
- **Verilog**
- **LaTeX**
- **Verilog**
- 2025-08
- **CUDA**
- 2025-10
- **CUDA**
- **Triton**
- **Verilog**
- **CUDA**
- **CUDA**
- **CUDA**
- **Triton**
- **Verilog**
- **CUDA, HIP, HLSL**
- **Verilog**
- **Haskell, Ocaml, Scala**
- **Verilog, Chisel, HLS, VHDL**
- 2026-01
-
-
6. Datasets
-
7. User-LLM Interaction
-
Others
-
-
Other Awesome LLM Reading Lists
-
8.2 Benchmarks
-
Programming Languages
Categories
5. Methods/Models for Downstream Tasks
1,213
8. Datasets
561
3. When Coding Meets Reasoning
294
2. Models
266
6. Analysis of AI-Generated Code
244
4. Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages
109
7. Human-LLM Interaction
72
News
55
9. Recommended Readings
32
5. Datasets
29
4. Datasets
20
1. Surveys
17
6. Datasets
4
Other Awesome LLM Reading Lists
3
Star History
2
7. User-LLM Interaction
1
Sub Categories
8.2 Benchmarks
591
3.5 Frontend Navigation
164
Text-To-SQL
163
Vulnerability Detection
115
Others
112
3.3 Code Agents
100
2.1 Base LLMs and Pretraining Strategies
92
Code Generation
88
Code Commenting and Summarization
83
Test Generation
78
Program Repair
74
Malicious Code Detection
73
2.4 (Instruction) Fine-Tuning on Code
71
3.1 Coding for Reasoning
66
Security and Vulnerabilities
58
3.4 Interactive Coding
55
2.3 General Pretraining on Code
50
Code Translation
47
Code Review
46
Frontend Development
44
Repository-Level Coding
41
2.5 Reinforcement Learning on Code
39
Code Similarity and Embedding (Clone Detection, Code Search)
37
Correctness
34
5.2 Benchmarks
30
Requirement Engineering
28
Program Proof
26
Log Analysis
26
AI-Generated Code Detection
25
Compiler Optimization
24
Automated Machine Learning
24
Code Refactoring and Migration
23
Issue Resolution
23
Code RAG
23
Binary Analysis and Decompilation
23
4.2 Benchmarks
20
Efficiency
20
3.2 Code Simulation
18
Code Ranking
16
Code QA & Reasoning
16
Software Configuration
16
Oracle Generation
15
Robustness
15
2.2 Existing LLM Adapted to Code
14
Interpretability
12
Fuzz Testing
12
Hallucination
12
API Usage
10
Software Modeling
10
Privacy
9
Commit Message Generation
8
Bias
7
Mutation Testing
7
8.1 Pretraining
6
Type Prediction
4
6.2 Benchmarks
4
Contamination
3