This is the repo for our TMLR survey Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code - a comprehensive review of LLM researches for code. Works in each category are ordered chronologically. If you have a basic understanding of machine learning but are new to NLP, we also provide a list of recommended readings in section 9.

🔥🔥🔥 [2024/10/15] Featured papers:

• 🔥🔥 Effi-Code: Unleashing Code Efficiency in Language Models from The University of Hong Kong.

• 🔥🔥 Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code from Purdue University.

• 🔥🔥 Unraveling the Potential of Large Language Models in Code Translation: How Far Are We? from Shanghai Jiao Tong University.

• 🔥🔥 LogLM: From Task-based to Instruction-based Automated Log Analysis from Huawei.

• 🔥🔥 REDO: Execution-Free Runtime Error Detection for COding Agents from AWS AI Labs.

• 🔥🔥 Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models from Queen’s University.

• 🔥 MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code from The Chinese University of Hong Kong.

• 🔥 What Makes Large Language Models Reason in (Multi-Turn) Code Generation? from Meta AI.

• 🔥 Mitigating Gender Bias in Code Large Language Models via Model Editing from Harbin Institute of Technology.

• 🔥 Do Current Language Models Support Code Intelligence for R Programming Language? from University of British Columbia.

🔥🔥🔥 [2024/09/06] Our survey has been accepted for publication by Transactions on Machine Learning Research (TMLR).

🔥🔥     [2024/09/24] We added a new section about program proof.

🔥         [2024/09/14] We have compiled 57 papers from August 2024 (including 48 presented at ACL 2024) in one WeChat article.

1. Surveys

2. Models

   2.1 Base LLMs and Pretraining Strategies

   2.2 Existing LLM Adapted to Code

   2.3 General Pretraining on Code

   • Encoder
   • Decoder
   • Encoder-Decoder
   • UniLM

   2.4 (Instruction) Fine-Tuning on Code

   2.5 Reinforcement Learning on Code

3. When Coding Meets Reasoning

   3.1 Coding for Reasoning

   3.2 Code Simulation

   3.3 Code Agents

   3.4 Interactive Coding

   3.5 Frontend Navigation

4. Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages

5. Methods/Models for Downstream Tasks

   • Programming

     • Code Generation
     • Code Ranking
     • Code Translation
     • Code Commenting and Summarization
     • Program Repair
     • Code Similarity and Embedding (Clone Detection, Code Search)
     • Type Prediction
     • Repository-Level Coding
     • Frontend Development
     • Text-To-SQL
     • Program Proof

   • Testing and Deployment

     • Test Generation
     • Oracle Generation
     • Mutation Testing
     • Fuzz Testing
     • Vulnerability Detection
     • Malicious Code Detection
     • Compiler Optimization
     • Decompilation

   • DevOps

     • Commit Message Generation
     • Code Review
     • Log Analysis

   • Requirement

     • Software Modeling
     • Requirement Engineering

6. Analysis of AI-Generated Code

   • Security and Vulnerabilities
   • Correctness
   • Hallucination
   • Efficiency
   • Robustness
   • Interpretability
   • API Usage
   • Privacy
   • AI-Generated Code Detection
   • Others

7. User-LLM Interaction

8. Datasets

   8.1 Pretraining

   8.2 Benchmarks

   • Integrated Benchmarks
   • Program Synthesis
   • Visually Grounded Program Synthesis
   • Code Reasoning and QA
   • Text-to-SQL
   • Code Translation
   • Program Repair
   • Code Summarization
   • Defect/Vulnerability Detection
   • Code Retrieval
   • Type Inference
   • Commit Message Generation
   • Repo-Level Coding

9. Recommended Readings

10. Citation

11. Star History

12. Join Us

We list several recent surveys on similar topics. While they are all about language models for code, 1-2 focus on NLP side; 3-6 focus on SE side; 7-11 are released after ours.

1. "Large Language Models Meet NL2Code: A Survey" [2022-12] [ACL 2023] [paper]

2. "A Survey on Pretrained Language Models for Neural Code Intelligence" [2022-12] [paper]

3. "An Empirical Comparison of Pre-Trained Models of Source Code" [2023-02] [ICSE 2023] [paper]

4. "Large Language Models for Software Engineering: A Systematic Literature Review" [2023-08] [paper]

5. "Towards an Understanding of Large Language Models in Software Engineering Tasks" [2023-08] [paper]

6. "Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey" [2023-10] [paper]

7. "A Survey on Large Language Models for Software Engineering" [2023-12] [paper]

8. "Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit" [2023-12] [paper]

9. "A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond" [2024-03] [paper]

10. "Tasks People Prompt: A Taxonomy of LLM Downstream Tasks in Software Verification and Falsification Approaches" [2024-04] [paper]

11. "Automatic Programming: Large Language Models and Beyond" [2024-05] [paper]

12. "Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models" [2024-10] [paper]

These LLMs are not specifically trained for code, but have demonstrated varying coding capability.

1. LaMDA: "LaMDA: Language Models for Dialog Applications" [2022-01] [paper]

2. PaLM: "PaLM: Scaling Language Modeling with Pathways" [2022-04] [JMLR] [paper]

3. GPT-NeoX: "GPT-NeoX-20B: An Open-Source Autoregressive Language Model" [2022-04] [ACL 2022 Workshop on Challenges & Perspectives in Creating LLMs] [paper] [repo]

4. BLOOM: "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model" [2022-11] [paper] [model]

5. LLaMA: "LLaMA: Open and Efficient Foundation Language Models" [2023-02] [paper]

6. GPT-4: "GPT-4 Technical Report" [2023-03] [paper]

7. LLaMA 2: "Llama 2: Open Foundation and Fine-Tuned Chat Models" [2023-07] [paper] [repo]

8. Phi-1.5: "Textbooks Are All You Need II: phi-1.5 technical report" [2023-09] [paper] [model]

9. Baichuan 2: "Baichuan 2: Open Large-scale Language Models" [2023-09] [paper] [repo]

10. Qwen: "Qwen Technical Report" [2023-09] [paper] [repo]

11. Mistral: "Mistral 7B" [2023-10] [paper] [repo]

12. Gemini: "Gemini: A Family of Highly Capable Multimodal Models" [2023-12] [paper]

13. Phi-2: "Phi-2: The surprising power of small language models" [2023-12] [blog]

14. YAYI2: "YAYI 2: Multilingual Open-Source Large Language Models" [2023-12] [paper] [repo]

15. DeepSeek: "DeepSeek LLM: Scaling Open-Source Language Models with Longtermism" [2024-01] [paper] [repo]

16. Mixtral: "Mixtral of Experts" [2024-01] [paper] [blog]

17. DeepSeekMoE: "DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models" [2024-01] [paper] [repo]

18. Orion: "Orion-14B: Open-source Multilingual Large Language Models" [2024-01] [paper] [repo]

19. OLMo: "OLMo: Accelerating the Science of Language Models" [2024-02] [paper] [repo]

20. Gemma: "Gemma: Open Models Based on Gemini Research and Technology" [2024-02] [paper] [blog]

21. Claude 3: "The Claude 3 Model Family: Opus, Sonnet, Haiku" [2024-03] [paper] [blog]

22. Yi: "Yi: Open Foundation Models by 01.AI" [2024-03] [paper] [repo]

23. Poro: "Poro 34B and the Blessing of Multilinguality" [2024-04] [paper] [model]

24. JetMoE: "JetMoE: Reaching Llama2 Performance with 0.1M Dollars" [2024-04] [paper] [repo]

25. LLaMA 3: "The Llama 3 Herd of Models" [2024-04] [blog] [repo] [paper]

26. Reka Core: "Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models" [2024-04] [paper]

27. Phi-3: "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone" [2024-04] [paper]

28. OpenELM: "OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework" [2024-04] [paper] [repo]

29. Tele-FLM: "Tele-FLM Technical Report" [2024-04] [paper] [model]

30. DeepSeek-V2: "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model" [2024-05] [paper] [repo]

31. GECKO: "GECKO: Generative Language Model for English, Code and Korean" [2024-05] [paper] [model]

32. MAP-Neo: "MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series" [2024-05] [paper] [repo]

33. Skywork-MoE: "Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models" [2024-06] [paper]

34. Xmodel-LM: "Xmodel-LM Technical Report" [2024-06] [paper]

35. GEB: "GEB-1.3B: Open Lightweight Large Language Model" [2024-06] [paper]

36. HARE: "HARE: HumAn pRiors, a key to small language model Efficiency" [2024-06] [paper]

37. DCLM: "DataComp-LM: In search of the next generation of training sets for language models" [2024-06] [paper]

38. Nemotron-4: "Nemotron-4 340B Technical Report" [2024-06] [paper]

39. ChatGLM: "ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools" [2024-06] [paper]

40. YuLan: "YuLan: An Open-source Large Language Model" [2024-06] [paper]

41. Gemma 2: "Gemma 2: Improving Open Language Models at a Practical Size" [2024-06] [paper]

42. H2O-Danube3: "H2O-Danube3 Technical Report" [2024-07] [paper]

43. Qwen2: "Qwen2 Technical Report" [2024-07] [paper]

44. ALLaM: "ALLaM: Large Language Models for Arabic and English" [2024-07] [paper]

45. SeaLLMs 3: "SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages" [2024-07] [paper]

46. AFM: "Apple Intelligence Foundation Language Models" [2024-07] [paper]

47. "To Code, or Not To Code? Exploring Impact of Code in Pre-training" [2024-08] [paper]

48. OLMoE: "OLMoE: Open Mixture-of-Experts Language Models" [2024-09] [paper]

49. "How Does Code Pretraining Affect Language Model Task Performance?" [2024-09] [paper]

50. EuroLLM: "EuroLLM: Multilingual Language Models for Europe" [2024-09] [paper]

51. "Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?" [2024-10] [paper]

These models are general-purpose LLMs further pretrained on code-related data.

• Codex (GPT-3): "Evaluating Large Language Models Trained on Code" [2021-07] [paper]

• PaLM Coder (PaLM): "PaLM: Scaling Language Modeling with Pathways" [2022-04] [JMLR] [paper]

• Minerva (PaLM): "Solving Quantitative Reasoning Problems with Language Models" [2022-06] [paper]

• PaLM 2 * (PaLM 2): "PaLM 2 Technical Report" [2023-05] [paper]

• Code LLaMA (LLaMA 2): "Code Llama: Open Foundation Models for Code" [2023-08] [paper] [repo]

• Lemur (LLaMA 2): "Lemur: Harmonizing Natural Language and Code for Language Agents" [2023-10] [ICLR 2024 Spotlight] [paper]

• BTX (LLaMA 2): "Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM" [2024-03] [paper]

• HiRoPE: "HiRoPE: Length Extrapolation for Code Models Using Hierarchical Position" [2024-03] [ACL 2024] [paper]

• "Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models" [2024-03] [paper]

• CodeGemma: "CodeGemma: Open Code Models Based on Gemma" [2024-04] [paper] [model]

• DeepSeek-Coder-V2: "DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence" [2024-06] [paper]

• "Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization" [2024-09] [paper]

• Qwen2.5-Coder: "Qwen2.5-Coder Technical Report" [2024-09] [paper]

These models are Transformer encoders, decoders, and encoder-decoders pretrained from scratch using existing objectives for general language modeling.

1. CuBERT (MLM + NSP): "Learning and Evaluating Contextual Embedding of Source Code" [2019-12] [ICML 2020] [paper] [repo]

2. CodeBERT (MLM + RTD): "CodeBERT: A Pre-Trained Model for Programming and Natural Languages" [2020-02] [EMNLP 2020 findings] [paper] [repo]

3. GraphCodeBERT (MLM + DFG Edge Prediction + DFG Node Alignment): "GraphCodeBERT: Pre-training Code Representations with Data Flow" [2020-09] [ICLR 2021] [paper] [repo]

4. SynCoBERT (MLM + Identifier Prediction + AST Edge Prediction + Contrastive Learning): "SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation" [2021-08] [paper]

5. DISCO (MLM + Node Type MLM + Contrastive Learning): "Towards Learning (Dis)-Similarity of Source Code from Program Contrasts" [2021-10] [ACL 2022] [paper]

6. Code-MVP (MLM + Type Inference + Contrastive Learning): "CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training" [2022-05] [NAACL 2022 Technical Track] [paper]

7. CodeSage (MLM + Deobfuscation + Contrastive Learning): "Code Representation Learning At Scale" [2024-02] [ICLR 2024] [paper]

8. CoLSBERT (MLM): "Scaling Laws Behind Code Understanding Model" [2024-02] [paper]

1. GPT-C (CLM): "IntelliCode Compose: Code Generation Using Transformer" [2020-05] [ESEC/FSE 2020] [paper]

2. CodeGPT (CLM): "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [2021-02] [NeurIPS Datasets and Benchmarks 2021] [paper] [repo]

3. CodeParrot (CLM) [2021-12] [blog]

4. PolyCoder (CLM): "A Systematic Evaluation of Large Language Models of Code" [2022-02] [DL4C@ICLR 2022] [paper] [repo]

5. CodeGen (CLM): "CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis" [2022-03] [ICLR 2023] [paper] [repo]

6. InCoder (Causal Masking): "InCoder: A Generative Model for Code Infilling and Synthesis" [2022-04] [ICLR 2023] [paper] [repo]

7. PyCodeGPT (CLM): "CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation" [2022-06] [IJCAI-ECAI 2022] [paper] [repo]

8. PanGu-Coder (CLM): "PanGu-Coder: Program Synthesis with Function-Level Language Modeling" [2022-07] [paper]

9. SantaCoder (FIM): "SantaCoder: don't reach for the stars!" [2023-01] [paper] [model]

10. CodeGeeX (CLM): "CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [2023-03] [paper] [repo]

11. StarCoder (FIM): "StarCoder: may the source be with you!" [2023-05] [paper] [model]

12. Phi-1 (CLM): "Textbooks Are All You Need" [2023-06] [paper] [model]

13. CodeFuse (CLM): "CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model" [2023-10] [paper] [model]

14. DeepSeek Coder (CLM+FIM): "DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence" [2024-01] [paper] [repo]

15. StarCoder2 (CLM+FIM): "StarCoder 2 and The Stack v2: The Next Generation" [2024-02] [paper] [repo]

16. CodeShell (CLM+FIM): "CodeShell Technical Report" [2024-03] [paper] [repo]

17. CodeQwen1.5 [2024-04] [blog]

18. Granite: "Granite Code Models: A Family of Open Foundation Models for Code Intelligence" [2024-05] [paper] "Scaling Granite Code Models to 128K Context" [2024-07] [paper]

19. NT-Java: "Narrow Transformer: Starcoder-Based Java-LM For Desktop" [2024-07] [paper]

20. Arctic-SnowCoder: "Arctic-SnowCoder: Demystifying High-Quality Data in Code Pretraining" [2024-09] [paper]

1. PyMT5 (Span Corruption): "PyMT5: multi-mode translation of natural language and Python code with transformers" [2020-10] [EMNLP 2020] [paper]

2. Mastropaolo et al. (MLM + Deobfuscation): "DOBF: A Deobfuscation Pre-Training Objective for Programming Languages" [2021-02] [ICSE 2021] [paper] [repo]

3. DOBF (Span Corruption): "Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks" [2021-02] [NeurIPS 2021] [paper] [repo]

4. PLBART (DAE): "Unified Pre-training for Program Understanding and Generation" [2021-03] [NAACL 2021] [paper] [repo]

5. CodeT5 (Span Corruption + Identifier Tagging + Masked Identifier Prediction + Text2Code + Code2Text): "CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation" [2021-09] [EMNLP 2021] [paper] [repo]

6. SPT-Code (Span Corruption + NSP + Method Name Prediction): "SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations" [2022-01] [ICSE 2022 Technical Track] [paper]

7. AlphaCode (MLM + CLM): "Competition-Level Code Generation with AlphaCode" [2022-02] [Science] [paper] [blog]

8. NatGen (Code Naturalization): "NatGen: Generative pre-training by "Naturalizing" source code" [2022-06] [ESEC/FSE 2022] [paper] [repo]

9. ERNIE-Code (Span Corruption + Pivot-based Translation LM): "ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages" [2022-12] [ACL23 (Findings)] [paper][repo]

10. CodeT5+ (Span Corruption + CLM + Text-Code Contrastive Learning + Text-Code Translation): "CodeT5+: Open Code Large Language Models for Code Understanding and Generation" [2023-05] [EMNLP 2023] [paper] [repo]

11. AST-T5 (Span Corruption): "AST-T5: Structure-Aware Pretraining for Code Generation and Understanding" [2024-01] [ICML 2024] [paper]

1. CugLM (MLM + NSP + CLM): "Multi-task Learning based Pre-trained Language Model for Code Completion" [2020-12] [ASE 2020] [paper]

2. UniXcoder (MLM + NSP + CLM + Span Corruption + Contrastive Learning + Code2Text): "UniXcoder: Unified Cross-Modal Pre-training for Code Representation" [2022-03] [ACL 2022] [paper] [repo]

These models apply Instruction Fine-Tuning techniques to enhance the capacities of Code LLMs.

1. WizardCoder (StarCoder + Evol-Instruct): "WizardCoder: Empowering Code Large Language Models with Evol-Instruct" [2023-06] [ICLR 2024] [paper] [repo]

2. PanGu-Coder 2 (StarCoder + Evol-Instruct + RRTF): "PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback" [2023-07] [paper]

3. OctoCoder (StarCoder) / OctoGeeX (CodeGeeX2): "OctoPack: Instruction Tuning Code Large Language Models" [2023-08] [ICLR 2024 Spotlight] [paper] [repo]

4. "At Which Training Stage Does Code Data Help LLMs Reasoning" [2023-09] [ICLR 2024 Spotlight] [paper]

5. InstructCoder: "InstructCoder: Instruction Tuning Large Language Models for Code Editing" [paper] [repo]

6. MFTCoder: "MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning" [2023-11] [KDD 2024] [paper] [repo]

7. "LLM-Assisted Code Cleaning For Training Accurate Code Generators" [2023-11] [ICLR 2024] [paper]

8. Magicoder: "Magicoder: Empowering Code Generation with OSS-Instruct" [2023-12] [ICML 2024] [paper]

9. WaveCoder: "WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning" [2023-12] [ACL 2024] [paper]

10. Astraios: "Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models" [2024-01] [paper]

11. DolphCoder: "DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning" [2024-02] [ACL 2024] [paper]

12. SafeCoder: "Instruction Tuning for Secure Code Generation" [2024-02] [ICML 2024] [paper]

13. "Code Needs Comments: Enhancing Code LLMs with Comment Augmentation" [ACL 2024 Findings] [paper]

14. CCT: "Code Comparison Tuning for Code Large Language Models" [2024-03] [paper]

15. SAT: "Structure-aware Fine-tuning for Code Pre-trained Models" [2024-04] [paper]

16. CodeFort: "CodeFort: Robust Training for Code Generation Models" [2024-04] [paper]

17. XFT: "XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts" [2024-04] [ACL 2024] [paper] [repo]

18. AIEV-Instruct: "AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct" [2024-05] [paper]

19. AlchemistCoder: "AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data" [2024-05] [paper]

20. "From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers" [2024-05] [paper]

21. "Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning" [2024-05] [paper]

22. PLUM: "PLUM: Preference Learning Plus Test Cases Yields Better Code Language Models" [2024-06] [paper]

23. mCoder: "McEval: Massively Multilingual Code Evaluation" [2024-06] [paper]

24. "Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models" [2024-06] [paper]

25. Code-Optimise: "Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency" [2024-06] [paper]

26. UniCoder: "UniCoder: Scaling Code Large Language Model via Universal Code" [2024-06] [ACL 2024] [paper]

27. "Brevity is the soul of wit: Pruning long files for code generation" [2024-06] [paper]

28. "Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning" [2024-07] [paper]

29. InverseCoder: "InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct" [2024-07] [paper]

30. "Curriculum Learning for Small Code Language Models" [2024-07] [paper]

31. Genetic-Instruct: "Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models" [2024-07] [paper]

32. DataScope: "API-guided Dataset Synthesis to Finetune Large Code Models" [2024-08] [paper]

33. ** XCoder**: "How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data" [2024-09] [paper]

34. GALLa: "GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding" [2024-09] [paper]

35. HexaCoder: "HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data" [2024-09] [paper]

36. AMR-Evol: "AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation" [2024-10] [paper]

37. LintSeq: "Training Language Models on Synthetic Edit Sequences Improves Code Synthesis" [2024-10] [paper]

38. CoBa: "CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models" [2024-10] [EMNLP 2024] [paper]

39. CursorCore: "CursorCore: Assist Programming through Aligning Anything" [2024-10] [paper]

1. CompCoder: "Compilable Neural Code Generation with Compiler Feedback" [2022-03] [ACL 2022] [paper]

2. CodeRL: "CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning" [2022-07] [NeurIPS 2022] [paper] [repo]

3. PPOCoder: "Execution-based Code Generation using Deep Reinforcement Learning" [2023-01] [TMLR 2023] [paper] [repo]

4. RLTF: "RLTF: Reinforcement Learning from Unit Test Feedback" [2023-07] [paper] [repo]

5. B-Coder: "B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis" [2023-10] [ICLR 2024] [paper]

6. IRCoCo: "IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion" [2024-01] [FSE 2024] [paper]

7. StepCoder: "StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback" [2024-02] [ACL 2024] [paper]

8. RLPF & DPA: "Performance-Aligned LLMs for Generating Fast Code" [2024-04] [paper]

9. "Measuring memorization in RLHF for code completion" [2024-06] [paper]

10. "Applying RLAIF for Code Generation with API-usage in Lightweight LLMs" [2024-06] [paper]

11. RLCoder: "RLCoder: Reinforcement Learning for Repository-Level Code Completion" [2024-07] [paper]

12. PF-PPO: "Policy Filtration in RLHF to Fine-Tune LLM for Code Generation" [2024-09] [paper]

13. Coffee-Gym: "Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code" [2024-09] [paper]

14. RLEF: "RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning" [2024-10] [paper]

15. CodePMP: "CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoning" [2024-10] [paper]

16. CodeDPO: "CodeDPO: Aligning Code Models with Self Generated and Verified Source Code" [2024-10] [paper]

1. PAL: "PAL: Program-aided Language Models" [2022-11] [ICML 2023] [paper] [repo]

2. PoT: "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks" [2022-11] [TMLR 2023] [paper] [repo]

3. CSV: "Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification" [2023-08] [ICLR 2024] [paper]

4. MathCoder: "MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning" [2023-10] [ICLR 2024] [paper]

5. CoC: "Chain of Code: Reasoning with a Language Model-Augmented Code Emulator" [2023-12] [ICML 2024] [paper]

6. MARIO: "MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline" [2024-01] [ACL 2024 Findings] [paper]

7. ReGAL: "ReGAL: Refactoring Programs to Discover Generalizable Abstractions" [2024-01] [ICML 2024] [paper]

8. "Executable Code Actions Elicit Better LLM Agents" [2024-02] [ICML 2024] [paper]

9. HProPro: "Exploring Hybrid Question Answering via Program-based Prompting" [2024-02] [ACL 2024] [paper]

10. xSTREET: "Eliciting Better Multilingual Structured Reasoning from LLMs through Code" [2024-03] [ACL 2024] [paper]

11. FlowMind: "FlowMind: Automatic Workflow Generation with LLMs" [2024-03] [paper]

12. Think-and-Execute: "Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models" [2024-04] [paper]

13. CoRE: "CoRE: LLM as Interpreter for Natural Language Programming, Pseudo-Code Programming, and Flow Programming of AI Agents" [2024-05] [paper]

14. MuMath-Code: "MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning" [2024-05] [paper]

15. COGEX: "Learning to Reason via Program Generation, Emulation, and Search" [2024-05] [paper]

16. "Arithmetic Reasoning with LLM: Prolog Generation & Permutation" [2024-05] [paper]

17. "Can LLMs Reason in the Wild with Programs?" [2024-06] [paper]

18. DotaMath: "DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning" [2024-07] [paper]

19. CIBench: "CIBench: Evaluating Your LLMs with a Code Interpreter Plugin" [2024-07] [paper]

20. PyBench: "PyBench: Evaluating LLM Agent on various real-world coding tasks" [2024-07] [paper]

21. AdaCoder: "AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering" [2024-07] [paper]

22. PyramidCoder: "Pyramid Coder: Hierarchical Code Generator for Compositional Visual Question Answering" [2024-07] [paper]

23. CodeGraph: "CodeGraph: Enhancing Graph Reasoning of LLMs with Code" [2024-08] [paper]

24. SIaM: "SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models" [2024-08] [paper]

25. CodePlan: "CodePlan: Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning" [2024-09] [paper]

26. PoT: "Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning" [2024-09] [paper]

27. MetaMath: "MetaMath: Integrating Natural Language and Code for Enhanced Mathematical Reasoning in Large Language Models" [2024-09] [paper]

28. "BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data" [2024-10] [paper]

29. CodeSteer: "Steering Large Language Models between Code Execution and Textual Reasoning" [2024-10] [paper]

30. MathCoder2: "MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code" [2024-10] [paper]

• "Code Simulation Challenges for Large Language Models" [2024-01] [paper]

• "CodeMind: A Framework to Challenge Large Language Models for Code Reasoning" [2024-02] [paper]

• "Executing Natural Language-Described Algorithms with Large Language Models: An Investigation" [2024-02] [paper]

• "Can Language Models Pretend Solvers? Logic Code Simulation with LLMs" [2024-03] [paper]

• "Evaluating Large Language Models with Runtime Behavior of Program Execution" [2024-03] [paper]

• "NExT: Teaching Large Language Models to Reason about Code Execution" [2024-04] [ICML 2024] [paper]

• "SelfPiCo: Self-Guided Partial Code Execution with LLMs" [2024-07] [paper]

• "Large Language Models as Code Executors: An Exploratory Study" [2024-10] [paper]

1. Self-collaboration: "Self-collaboration Code Generation via ChatGPT" [2023-04] [paper]

2. ChatDev: "Communicative Agents for Software Development" [2023-07] [paper] [repo]

3. MetaGPT: "MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework" [2023-08] [paper] [repo]

4. CodeChain: "CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules" [2023-10] [ICLR 2024] [paper]

5. CodeAgent: "CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges" [2024-01] [ACL 2024] [paper]

6. CONLINE: "CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing" [2024-03] [paper]

7. LCG: "When LLM-based Code Generation Meets the Software Development Process" [2024-03] [paper]

8. RepairAgent: "RepairAgent: An Autonomous, LLM-Based Agent for Program Repair" [2024-03] [paper]

9. MAGIS:: "MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution" [2024-03] [paper]

10. SoA: "Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization" [2024-04] [paper]

11. AutoCodeRover: "AutoCodeRover: Autonomous Program Improvement" [2024-04] [paper]

12. SWE-agent: "SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering" [2024-05] [paper]

13. MapCoder: "MapCoder: Multi-Agent Code Generation for Competitive Problem Solving" [2024-05] [ACL 2024] [paper]

14. "Fight Fire with Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?" [2024-05] [paper]

15. FunCoder: "Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation" [2024-05] [paper]

16. CTC: "Multi-Agent Software Development through Cross-Team Collaboration" [2024-06] [paper]

17. MASAI: "MASAI: Modular Architecture for Software-engineering AI Agents" [2024-06] [paper]

18. AgileCoder: "AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology" [2024-06] [paper]

19. CodeNav: "CodeNav: Beyond tool-use to using real-world codebases with LLM agents" [2024-06] [paper]

20. INDICT: "INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness" [2024-06] [paper]

21. AppWorld: "AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents" [2024-07] [paper]

22. CortexCompile: "CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis" [2024-08] [paper]

23. Survey: "Large Language Model-Based Agents for Software Engineering: A Survey" [2024-09] [paper]

24. AutoSafeCoder: "AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing" [2024-09] [paper]

25. SuperCoder2.0: "SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer" [2024-09] [paper]

26. Survey: "Agents in Software Engineering: Survey, Landscape, and Vision" [2024-09] [paper]

27. MOSS: "MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents" [2024-09] [paper]

28. HyperAgent: "HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale" [2024-09] [paper]

29. "Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective" [2024-09] [paper]

30. RGD: "RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance" [2024-10] [paper]

31. AutoML-Agent: "AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML" [2024-10] [paper]

32. Seeker: "Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent Approach" [2024-10] [paper]

33. REDO: "REDO: Execution-Free Runtime Error Detection for COding Agents" [2024-10] [paper]

• "Interactive Program Synthesis" [2017-03] [paper]

• "Question selection for interactive program synthesis" [2020-06] [PLDI 2020] [paper]

• "Interactive Code Generation via Test-Driven User-Intent Formalization" [2022-08] [paper]

• "Improving Code Generation by Training with Natural Language Feedback" [2023-03] [TMLR] [paper]

• "Self-Refine: Iterative Refinement with Self-Feedback" [2023-03] [NeurIPS 2023] [paper]

• "Teaching Large Language Models to Self-Debug" [2023-04] [paper]

• "Self-Edit: Fault-Aware Code Editor for Code Generation" [2023-05] [ACL 2023] [paper]

• "LeTI: Learning to Generate from Textual Interactions" [2023-05] [paper]

• "Is Self-Repair a Silver Bullet for Code Generation?" [2023-06] [ICLR 2024] [paper]

• "InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback" [2023-06] [NeurIPS 2023] [paper]

• "INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair" [2023-11] [ACL 2024 Findings] [paper]

• "OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement" [2024-02] [ACL 2024 Findings] [paper]

• "Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback" [2024-03] [ACL 2024 Findings] [paper]

• "CYCLE: Learning to Self-Refine the Code Generation" [2024-03] [paper]

• "LLM-based Test-driven Interactive Code Generation: User Study and Empirical Evaluation" [2024-04] [paper]

• "SOAP: Enhancing Efficiency of Generated Code via Self-Optimization" [2024-05] [paper]

• "Code Repair with LLMs gives an Exploration-Exploitation Tradeoff" [2024-05] [paper]

• "ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation" [2024-05] [paper]

• "Training LLMs to Better Self-Debug and Explain Code" [2024-05] [paper]

• "Requirements are All You Need: From Requirements to Code with LLMs" [2024-06] [paper]

• "I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation" [2024-07] [paper]

• "An Empirical Study on Self-correcting Large Language Models for Data Science Code Generation" [2024-08] [paper]

• "RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation" [2024-09] [paper]

• "From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging" [2024-10] [paper]

• "What Makes Large Language Models Reason in (Multi-Turn) Code Generation?" [2024-10] [paper]

• "MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding" [2021-10] [ACL 2022] [paper]

• "WebKE: Knowledge Extraction from Semi-structured Web with Pre-trained Markup Language Model" [2021-10] [CIKM 2021] [paper]

• "WebGPT: Browser-assisted question-answering with human feedback" [2021-12] [paper]

• "CM3: A Causal Masked Multimodal Model of the Internet" [2022-01] [paper]

• "DOM-LM: Learning Generalizable Representations for HTML Documents" [2022-01] [paper]

• "WebFormer: The Web-page Transformer for Structure Information Extraction" [2022-02] [WWW 2022] [paper]

• "A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility" [2022-02] [ECCV 2022] [paper]

• "WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents" [2022-07] [NeurIPS 2022] [paper]

• "Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding" [2022-10] [ICML 2023] [paper]

• "Understanding HTML with Large Language Models" [2022-10] [EMNLP 2023 findings] [paper]

• "WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics" [2023-01] [CHI 2023] [paper]

• "Mind2Web: Towards a Generalist Agent for the Web" [2023-06] [NeurIPS 2023] [paper]

• "A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis", [2023-07] [ICLR 2024] [paper]

• "WebArena: A Realistic Web Environment for Building Autonomous Agents" [2023-07] [paper]

• "CogAgent: A Visual Language Model for GUI Agents" [2023-12] [paper]

• "GPT-4V(ision) is a Generalist Web Agent, if Grounded" [2024-01] [paper]

• "WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models" [2024-01] [paper]

• "WebLINX: Real-World Website Navigation with Multi-Turn Dialogue" [2024-02] [paper]

• "OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web" [2024-02] [paper]

• "AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent" [2024-04] [paper]

• "WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents" [2024-04] [paper]

• "AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation" [2024-04] [paper]

• "GUICourse: From General Vision Language Models to Versatile GUI Agents" [2024-06] [paper]

• "NaviQAte: Functionality-Guided Web Application Navigation" [2024-09] [paper]

• "MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding" [2024-09] [paper]

• "Multimodal Auto Validation For Self-Refinement in Web Agents" [2024-10] [paper]

• "Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents" [2024-10] [paper]

• [Ruby] "On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages" [2022-04] [ICPC 2022] [paper]

• [Verilog] "Benchmarking Large Language Models for Automated Verilog RTL Code Generation" [2022-12] [DATE 2023] [paper]

• [OCL] "On Codex Prompt Engineering for OCL Generation: An Empirical Study" [2023-03] [MSR 2023] [paper]

• [Ansible-YAML] "Automated Code generation for Information Technology Tasks in YAML through Large Language Models" [2023-05] [DAC 2023] [paper]

• [Hansl] "The potential of LLMs for coding with low-resource and domain-specific programming languages" [2023-07] [paper]

• [Verilog] "VeriGen: A Large Language Model for Verilog Code Generation" [2023-07] [paper]

• [Verilog] "RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model" [2023-08] [paper]

• [Racket, OCaml, Lua, R, Julia] "Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs" [2023-08] [paper]

• [Verilog] "VerilogEval: Evaluating Large Language Models for Verilog Code Generation" [2023-09] [ICCAD 2023] [paper]

• [Verilog] "RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models" [2023-11] [paper]

• [Verilog] "Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis" [2023-12] [paper]

• [Verilog] "RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution" [2023-12] [paper]

• [Verilog] "BetterV: Controlled Verilog Generation with Discriminative Guidance" [2024-02] [ICML 2024] [paper]

• [R] "Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R" [2024-03] [paper]

• [Haskell] "Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study" [2024-03] [paper]

• [Verilog] "A Multi-Expert Large Language Model Architecture for Verilog Code Generation" [2024-04] [paper]

• [Verilog] "CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation" [2024-04] [paper]

• [Alloy] "An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications" [2024-04] [paper]

• [Verilog] "Evaluating LLMs for Hardware Design and Test" [2024-04] [paper]

• [Kotlin, Swift, and Rust] "Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT" [2024-04] [paper]

• [Verilog] "MEIC: Re-thinking RTL Debug Automation using LLMs" [2024-05] [paper]

• [Bash] "Tackling Execution-Based Evaluation for NL2Bash" [2024-05] [paper]

• [Fortran, Julia, Matlab, R, Rust] "Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust" [2024-05] [paper]

• [OpenAPI] "Optimizing Large Language Models for OpenAPI Code Completion" [2024-05] [paper]

• [Kotlin] "Kotlin ML Pack: Technical Report" [2024-05] [paper]

• [Verilog] "VerilogReader: LLM-Aided Hardware Test Generation" [2024-06] [paper]

• "Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming" [2024-06] [paper]

• [Logo] "Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment" [2024-06] [paper]

• [Ansible YAML, Bash] "DocCGen: Document-based Controlled Code Generation" [2024-06] [paper]

• [Qiskit] "Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models" [2024-06] [paper]

• [Perl, Golang, Swift] "DistiLRR: Transferring Code Repair for Low-Resource Programming Languages" [2024-06] [paper]

• [Verilog] "AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation" [2024-06] [paper]

• "A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation" [2024-07] [paper]

• [Json, XLM, YAML] "ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages" [2024-07] [paper]

• [Verilog] "AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design" [2024-07] [paper]

• [Verilog] "CodeV: Empowering LLMs for Verilog Generation through Multi-Level Summarization" [2024-07] [paper]

• [Verilog] "ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation" [2024-07] [paper]

• [Verilog] "OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection" [2024-07] [paper]

• [Verilog] "Large Language Model for Verilog Generation with Golden Code Feedback" [2024-07] [paper]

• [Verilog] "AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs" [2024-07] [paper]

• [RPA] "Plan with Code: Comparing approaches for robust NL to DSL generation" [2024-08] [paper]

• [Verilog] "VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool" [2024-08] [paper]

• [Verilog] "Revisiting VerilogEval: Newer LLMs, In-Context Learning, and Specification-to-RTL Tasks" [2024-08] [paper]

• [MaxMSP, Web Audio] "Benchmarking LLM Code Generation for Audio Programming with Visual Dataflow Languages" [2024-09] [paper]

• [Verilog] "RTLRewriter: Methodologies for Large Models aided RTL Code Optimization" [2024-09] [paper]

• [Verilog] "CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair" [2024-09] [paper]

• [Bash] "ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement" [2024-09] [paper]

• [Survey] "Survey on Code Generation for Low resource and Domain Specific Programming Languages" [2024-10] [paper]

• [R] "Do Current Language Models Support Code Intelligence for R Programming Language?" [2024-10] [paper]

• "Can Large Language Models Generate Geospatial Code?" [2024-10] [paper]

For each task, the first column contains non-neural methods (e.g. n-gram, TF-IDF, and (occasionally) static program analysis); the second column contains non-Transformer neural methods (e.g. LSTM, CNN, GNN); the third column contains Transformer based methods (e.g. BERT, GPT, T5).

• "Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency" [2023-09] [ACL 2024] [paper]

• "Self-Infilling Code Generation" [2023-11] [ICML 2024] [paper]

• "JumpCoder: Go Beyond Autoregressive Coder via Online Modification" [2024-01] [ACL 2024] [paper]

• "Unsupervised Evaluation of Code LLMs with Round-Trip Correctness" [2024-02] [ICML 2024] [paper]

• "The Larger the Better? Improved LLM Code-Generation via Budget Reallocation" [2024-03] [paper]

• "Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models" [2024-03] [ACL 2024] [paper]

• "Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective" [2024-04] [ACL 2024 Findings] [paper]

• "Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs" [2024-04] [paper]

• "Quality Assessment of Prompts Used in Code Generation" [2024-04] [paper]

• "Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation" [2024-04] [paper]

• "CodeGRAG: Extracting Composed Syntax Graphs for Retrieval Augmented Cross-Lingual Code Generation" [2024-05] [paper]

• "Large Language Models Synergize with Automated Machine Learning" [2024-05] [paper]

• "Prompt-based Code Completion via Multi-Retrieval Augmented Generation" [2024-05] [paper]

• "Model Cascading for Code: Reducing Inference Costs with Model Cascading for LLM Based Code Generation" [2024-05] [paper]

• "A Survey on Large Language Models for Code Generation" [2024-06] [paper]

• "A Lightweight Framework for Adaptive Retrieval In Code Completion With Critique Model" [2024-06] [papaer]

• "Is Programming by Example solved by LLMs?" [2024-06] [paper]

• "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" [2024-06] [paper]

• "MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning" [2024-06] [ACL 2024] [paper]

• "Revisiting the Impact of Pursuing Modularity for Code Generation" [2024-07] [paper]

• "Evaluating Long Range Dependency Handling in Code Generation Models using Multi-Step Key Retrieval" [2024-07] [paper]

• "When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention" [2024-07] [paper]

• "Assessing Programming Task Difficulty for Efficient Evaluation of Large Language Models" [2024-07] [paper]

• "ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models" [2024-08] [ACL 2024] [paper]

• "Fine-tuning Language Models for Joint Rewriting and Completion of Code with Potential Bugs" [2024-08] [ACL 2024 Findings] [paper]

• "Selective Prompt Anchoring for Code Generation" [2024-08] [paper]

• "Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer" [2024-08] [paper]

• "Optimizing Large Language Model Hyperparameters for Code Generation" [2024-08] [paper]

• "EPiC: Cost-effective Search-based Prompt Engineering of LLMs for Code Generation" [2024-08] [paper]

• "CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers" [2024-08] [paper]

• "No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair" [2024-09] [paper]

• "Planning In Natural Language Improves LLM Search For Code Generation" [2024-09] [paper]

• "Multi-Programming Language Ensemble for Code Generation in Large Language Model" [2024-09] [paper]

• "A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement" [2024-09] [paper]

• "USCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding" [2024-09] [paper]

• "Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation" [2024-09] [paper]

• "Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation" [2024-09] [paper]

• "Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity" [2024-09] [paper]

• "Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning" [2024-10] [paper]

• "Showing LLM-Generated Code Selectively Based on Confidence of LLMs" [2024-10] [paper]

• "AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generation" [2024-10] [paper]

• "Fault-Aware Neural Code Rankers" [2022-06] [NeurIPS 2022] [paper]

• "Functional Overlap Reranking for Neural Code Generation" [2023-10] [ACL 2024 Findings] [paper]

• "Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking" [2024-08] [paper]

• "DOCE: Finding the Sweet Spot for Execution-Based Code Generation" [2024-08] [paper]

• "Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates" [2024-08] [paper]

• "B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests" [2024-09] [paper]

• "Learning Code Preference via Synthetic Evolution" [2024-10] [paper]

• "Tree-to-tree Neural Networks for Program Translation" [2018-02] [NeurIPS 2018] [paper]

• "Program Language Translation Using a Grammar-Driven Tree-to-Tree Model" [2018-07] [paper]

• "Unsupervised Translation of Programming Languages" [2020-06] [NeurIPS 2020] [paper]

• "Leveraging Automated Unit Tests for Unsupervised Code Translation" [2021-10] [ICLR 2022] paper]

• "Code Translation with Compiler Representations" [2022-06] [ICLR 2023] [paper]

• "Multilingual Code Snippets Training for Program Translation" [2022-06] [AAAI 2022] [paper]

• "BabelTower: Learning to Auto-parallelized Program Translation" [2022-07] [ICML 2022] [paper]

• "Syntax and Domain Aware Model for Unsupervised Program Translation" [2023-02] [ICSE 2023] [paper]

• "CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution" [2023-06] [paper]

• "Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code" [2023-08] [ICSE 2024] [paper]

• "On the Evaluation of Neural Code Translation: Taxonomy and Benchmark", 2023-08, ASE 2023, [paper]

• "Program Translation via Code Distillation" [2023-10] [EMNLP 2023] [paper]

• "Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations" [2023-11] [EMNLP 2023 Findings] [paper]

• "Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation" [2024-03] [paper]

• "Exploring and Unleashing the Power of Large Language Models in Automated Code Translation" [2024-04] [paper]

• "VERT: Verified Equivalent Rust Transpilation with Few-Shot Learning" [2024-04] [paper]

• "Towards Translating Real-World Code with LLMs: A Study of Translating to Rust" [2024-05] [paper]

• "An interpretable error correction method for enhancing code-to-code translation" [2024-05] [ICLR 2024] [paper]

• "LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes" [2024-06] [paper]

• "Rectifier: Code Translation with Corrector via LLMs" [2024-07] [paper]

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• "An Empirical Evaluation of GitHub Copilot's Code Suggestions" [2022-05] [MSR 2022] [paper]

• "Large Language Models and Simple, Stupid Bugs" [2023-03] [MSR 2023] [paper]

• "Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT" [2023-04] [paper]

• "No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT" [2023-08] [paper]

• "The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?" [2024-02] [ACL 2024 Findings] [paper]

• "Bugs in Large Language Models Generated Code: An Empirical Study" [2024-03] [paper]

• "ChatGPT Incorrectness Detection in Software Reviews" [2024-03] [paper]

• "Validating LLM-Generated Programs with Metamorphic Prompt Testing" [2024-06] [paper]

• "Where Do Large Language Models Fail When Generating Code?" [2024-06] [paper]

• "GitHub Copilot: the perfect Code compLeeter?" [2024-06] [paper]

• "What's Wrong with Your Code Generated by Large Language Models? An Extensive Study" [2024-07] [paper]

• "Uncovering Weaknesses in Neural Code Generation" [2024-07] [paper]

• "Understanding Defects in Generated Codes by Language Models" [2024-08] [paper]

• "CodeSift: An LLM-Based Reference-Less Framework for Automatic Code Validation" [2024-08] [paper]

• "Examination of Code generated by Large Language Models" [2024-08] [paper]

• "Fixing Code Generation Errors for Large Language Models" [2024-09] [paper]

• "Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation" [2024-09] [paper]

• "Insights from Benchmarking Frontier Language Models on Web App Code Generation" [2024-09] [paper]

• "Evaluating the Performance of Large Language Models in Competitive Programming: A Multi-Year, Multi-Grade Analysis" [2024-09] [paper]

• "A Case Study of Web App Coding with OpenAI Reasoning Models" [2024-09] [paper]

• "CodeJudge: Evaluating Code Generation with Large Language Models" [2024-10] [paper]

• "Exploring and Evaluating Hallucinations in LLM-Powered Code Generation" [2024-04] [paper]

• "CodeHalu: Code Hallucinations in LLMs Driven by Execution-based Verification" [2024-04] [paper]

• "We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs" [2024-06] [paper]

• "Code Hallucination" [2024-07] [paper]

• "On Mitigating Code LLM Hallucinations with API Documentation" [2024-07] [paper]

• "CodeMirage: Hallucinations in Code Generated by Large Language Models" [2024-08] [paper]

• "LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation" [2024-09] [paper]

• "Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code" [2024-10] [paper]

• "On Evaluating the Efficiency of Source Code Generated by LLMs" [2024-04] [paper]

• "A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama" [2024-05] [paper]

• "From Effectiveness to Efficiency: Comparative Evaluation of Code Generated by LCGMs for Bilingual Programming Questions" [2024-06] [paper]

• "How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark" [2024-06] [paper]

• "ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?" [2024-07] [paper]

• "A Performance Study of LLM-Generated Code on Leetcode" [2024-07] [paper]

• "Evaluating Language Models for Efficient Code Generation" [2024-08] [paper]

• "Effi-Code: Unleashing Code Efficiency in Language Models" [2024-10] [paper]

• "Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain" [2023-10] [paper]

• "Do Large Code Models Understand Programming Concepts? A Black-box Approach" [2024-02] [ICML 2024] [paper]

• "Syntactic Robustness for LLM-based Code Generation" [2024-04] [paper]

• "NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations" [2024-06] [paper]

• "An Empirical Study on Capability of Large Language Models in Understanding Code Semantics" [2024-07] [paper]

• "A Critical Study of What Code-LLMs (Do Not) Learn" [2024-06] [ACL 2024 Findings] [paper]

• "Looking into Black Box Code Language Models" [2024-07] [paper]

• "DeepCodeProbe: Towards Understanding What Models Trained on Code Learn" [2024-07] [paper]

• "Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations" [2024-07] [paper]

• "How and Why LLMs Use Deprecated APIs in Code Completion? An Empirical Study" [2024-06] [paper]

• "Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations" [2024-08] [paper]

• "A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How" [2024-09] [paper]

• "AutoAPIEval: A Framework for Automated Evaluation of LLMs in API-Oriented Code Generation" [2024-09] [paper]

• "Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach" [2024-04] [paper]

• "CodeCipher: Learning to Obfuscate Source Code Against LLMs" [2024-10] [paper]

• "Decoding Secret Memorization in Code LLMs Through Token-Level Characterization" [2024-10] [paper]

• "Zero-Shot Detection of Machine-Generated Codes" [2023-10] [paper]

• "CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code" [2024-04] [paper]

• "ChatGPT Code Detection: Techniques for Uncovering the Source of Code" [2024-05] [paper]

• "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" [2024-05] [paper]

• "Automatic Detection of LLM-generated Code: A Case Study of Claude 3 Haiku" [2024-09] [paper]

• "Who Wrote this Code? Watermarking for Code Generation" [2023-05] [ACL 2024] [paper]

• "Testing the Effect of Code Documentation on Large Language Model Code Understanding" [2024-04] [paper]

• "Automated Creation of Source Code Variants of a Cryptographic Hash Function Implementation Using Generative Pre-Trained Transformer Models" [2024-04] [paper]

• "Exploring Multi-Lingual Bias of Large Code Models in Code Generation" [2024-04] [paper]

• "Evaluation of the Programming Skills of Large Language Models" [2024-05] [paper]

• "Where Are Large Language Models for Code Generation on GitHub?" [2024-06] [paper]

• "Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models" [2024-06] [paper]

• "Benchmarking Language Model Creativity: A Case Study on Code Generation" [2024-07] [paper]

• "Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models" [2024-07] [paper]

• "Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes" [2024-08] [paper]

• "Strategic Optimization and Challenges of Large Language Models in Object-Oriented Programming" [2024-08] [paper]

• "A Survey on Evaluating Large Language Models in Code Generation Tasks" [2024-08] [paper]

• "An exploratory analysis of Community-based Question-Answering Platforms and GPT-3-driven Generative AI: Is it the end of online community-based learning?" [2024-09] [paper]

• "Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B" [2024-09] [paper]

• "Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants" [2024-09] [paper]

• "Mitigating Gender Bias in Code Large Language Models via Model Editing" [2024-10] [paper]

• "Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models" [2022-04] [CHI EA 2022] [paper]

• "Grounded Copilot: How Programmers Interact with Code-Generating Models" [2022-06] [OOPSLA 2023] [paper]

• "Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming" [2022-10] [paper]

• "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot" [2023-02] [paper]

• "The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development" [2023-02] [IUI 2023] [paper]

• ""It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers" [2023-04] [ACM TCHI] [paper]

• "DevGPT: Studying Developer-ChatGPT Conversations" [2023-08] [paper]

• "How Do Analysts Understand and Verify AI-Assisted Data Analyses?" [2023-09] [paper]

• "How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment" [2023-09] [Koli Calling 2023] [paper]

• "Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities" [2023-10] [paper]

• "The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers" [2024-04] [paper]

• "Unlocking Adaptive User Experience with Generative AI" [2024-04] [paper]

• "BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks" [2024-04] [paper]

• "How far are AI-powered programming assistants from meeting developers' needs?" [2024-04] [paper]

• "Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice" [2024-04] [paper]

• "The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances" [2024-04] [paper]

• "amplified.dev: a living document that begins to sketch a vision for a future where developers are amplified, not automated" [2024-05] [paper]

• "Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches" [2024-05] [paper]

• "Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns" [2024-05] [paper]

• "Full Line Code Completion: Bringing AI to Desktop" [2024-05] [paper]

• "Developers' Perceptions on the Impact of ChatGPT in Software Development: A Survey" [2024-05] [paper]

• "A Transformer-Based Approach for Smart Invocation of Automatic Code Completion" [2024-05] [paper]

• "A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions" [2024-05] [paper]

• "Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT" [2024-05] [paper]

• "Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent" [2024-05] [paper]

• "Learning Task Decomposition to Assist Humans in Competitive Programming" [2024-06] [ACL 2024] [paper]

• "Impact of AI-tooling on the Engineering Workspace" [2024-06] [paper]

• "Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward" [2024-06] [paper]

• "Instruct, Not Assist: LLM-based Multi-Turn Planning and Hierarchical Questioning for Socratic Code Debugging" [2024-06] [paper]

• "Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects" [2024-06] [paper]

• "Let the Code LLM Edit Itself When You Edit the Code" [2024-07] [paper]

• "Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation" [2024-07] [paper]

• "How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course" [2024-07] [paper]

• "Can Developers Prompt? A Controlled Experiment for Code Documentation Generation" [2024-08] [paper]

• "The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies" [2024-09] [paper]

• "Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering" [2024-09] [paper]

• "The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot" [2024-09] [paper]

• ""I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development" [2024-09] [paper]

• "Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development" [2024-09] [paper]

• "Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks" [2024-10] [paper]

• "Code-Survey: An LLM-Driven Methodology for Analyzing Large-Scale Codebases" [2024-10] [paper]

• "The potential of LLM-generated reports in DevSecOps" [2024-10] [paper]

• "The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot" [2024-10] [paper]

• "Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning" [2024-10] [paper]

• "One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks" [2024-10] [paper]

1. CodeSearchNet: "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [2019-09] [paper] [repo] [data]

2. The Pile: "The Pile: An 800GB Dataset of Diverse Text for Language Modeling" [2020-12], [paper] [data]

3. CodeParrot, 2022-02, [data]

4. The Stack: "The Stack: 3 TB of permissively licensed source code" [2022-11] [paper] [data]

5. ROOTS: "The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset" [2023-03] [NeurIPS 2022 Datasets and Benchmarks Track] [paper] [data]

6. The Stack v2: "StarCoder 2 and The Stack v2: The Next Generation" [2024-02] [paper] [data]

• CodeXGLUE: "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [2021-02] [NeurIPS Datasets and Benchmarks 2021] [paper] [repo] [data]

• CodefuseEval: "CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model" [2023-10] [paper] [repo]

• CodeScope: "CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation" [2023-11] [ACL 2024] [paper] [repo]

• CodeEditorBench: "CodeEditorBench: Evaluating Code Editing Capability of Large Language Models" [2024-04] [paper] [repo]

• Long Code Arena: "Long Code Arena: a Set of Benchmarks for Long-Context Code Models" [2024-06] [paper] [repo]

• CodeRAG-Bench: "CodeRAG-Bench: Can Retrieval Augment Code Generation?" [2024-06] [paper] [repo]

• LiveBench: "LiveBench: A Challenging, Contamination-Free LLM Benchmark" [2024-06] [paper] [repo]

• DebugEval: "Enhancing the Code Debugging Ability of LLMs via Communicative Agent Based Data Refinement" [2024-08] [paper] [repo]

Date	Venue	Benchmark	Size	Language	Source
2018-02	LREC 2018	NL2Bash	9305	Bash	"NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System" [paper] [data]
2018-08	EMNLP 2018	CONCODE	104K	Java	"Mapping Language to Code in Programmatic Context" [paper] [data]
2019-10	EMNLP-IJCNLP 2019	JuICe	1.5M/3725 *	Python	"JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation" [paper] [data]
2021-05	NeurIPS 2021	APPS	10000	Python	"Measuring Coding Challenge Competence With APPS" [paper] [data]
2021-07	arXiv	HumanEval	164	Python	"Evaluating Large Language Models Trained on Code" [paper] [data]
2021-08	arXiv	MBPP/MathQA-Python	974/23914	Python	"Program Synthesis with Large Language Models" [paper] [MBPP] [MathQA-Python]
2021-08	ACL/IJCNLP 2021	PlotCoder	40797	Python	"PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context" [paper] [data]
2022-01	arXiv	DSP	1119	Python	"Training and Evaluating a Jupyter Notebook Data Science Assistant" [paper] [data]
2022-02	Science	CodeContests	13610	C++, Python, Java	"Competition-Level Code Generation with AlphaCode" [paper] [data]
2022-03	EACL 2023 Findings	MCoNaLa	896	Python	"MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages" [paper] [data]
2022-06	arXiv	AixBench	336	Java	"AixBench: A Code Generation Benchmark Dataset" [paper] [data]
2022-08	IEEE Trans. Software Engineering	MultiPL-E			"MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation", [paper] [data]
2022-10	ICLR 2023	MBXP	12.4K	Python, Java, JS, TypeScript, Go, C#, PHP, Ruby, Kotlin, C++, Perl, Scala, Swift	"Multi-lingual Evaluation of Code Generation Models" [paper] [data]
2022-10	ICLR 2023	Multilingual HumanEval	1.9K	Python, Java, JS, TypeScript, Go, C#, PHP, Ruby, Kotlin, Perl, Scala, Swift	"Multi-lingual Evaluation of Code Generation Models" [paper] [data]
2022-10	ICLR 2023	MathQA-X	5.6K	Python, Java, JS	"Multi-lingual Evaluation of Code Generation Models" [paper] [data]
2022-11	arXiv	ExeDS	534	Python	"Execution-based Evaluation for Data Science Code Generation Models" [paper] [data]
2022-11	arXiv	DS-1000	1000	Python	"DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation" [paper] [data]
2022-12	arXiv	ODEX	945	Python	"Execution-Based Evaluation for Open-Domain Code Generation" [paper] [data]
2023-02	arXiv	CoderEval	460	Python, Java	"CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models" [paper] [data]
2023-03	ACL 2024	xCodeEval	5.5M	C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust	"XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [paper] [data]
2023-03	arXiv	HumanEval-X	820	Python, C++, Java, JS, Go	"CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [paper] [data]
2023-05	arXiv	HumanEval+	164	Python	"Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation" [paper] [data]
2023-06	ACL 2024 Findings	StudentEval	1749 $^\dagger$	Python	"StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code" [paper] [data]
2023-08	ICLR 2024 Spotlight	HumanEvalPack	984	Python, JS, Go, Java, C++, Rust	"OctoPack: Instruction Tuning Code Large Language Models" [paper] [data]
2023-06	NeurIPS 2023	DotPrompts	10538 $^\ddagger$	Java	"Guiding Language Models of Code with Global Context using Monitors" [paper] [data]
2023-09	arXiv	CodeApex	476	C++	"CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models" [paper] [data]
2023-09	arXiv	VerilogEval	8645/156 $^\diamond$	Verilog	"VerilogEval: Evaluating Large Language Models for Verilog Code Generation" [paper] [data]
2023-11	arXiv	ML-Bench	10040	Bash	"ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks" [paper] [data]
2023-12	arXiv	TACO	26,433	Python	"TACO: Topics in Algorithmic COde generation dataset" [paper] [data]
2024-01	HPDC	ParEval	420	C++, CUDA, HIP	"Can Large Language Models Write Parallel Code?" [paper] [data]
2024-02	ACL 2024 Findings	OOP	431	Python	"OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models" [ppaer] [data]
2024-04	arXiv	USACO	307	Python	"Can Language Models Solve Olympiad Programming?" [paper] [data]
2024-04	LREC-COLING 2024	PECC	2396	Python	"PECC: Problem Extraction and Coding Challenges" [paper] [data]
2024-04	arXiv	CodeGuard+	23	Python, C	"Constrained Decoding for Secure Code Generation" [paper] [data]
2024-05	ACL 2024 Findings	NaturalCodeBench	402	Python, Java	"NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts" [paper] [data]
2024-05	arXiv	MHPP	140	Python	"MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation" [paper] [repo]
2024-06	arXiv	VHDL-Eval	202	VHDL	"VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation" [paper]
2024-06	arXiv	AICoderEval	492	Python	"AICoderEval: Improving AI Domain Code Generation of Large Language Models" [paper] [data]
2024-06	arXiv	VersiCode	98,692	Python	"VersiCode: Towards Version-controllable Code Generation" [paper] [data]
2024-06	IEEE AITest 2024	ScenEval	12,864	Java	"ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation" [paper]
2024-06	arXiv	BigCodeBench	1,140	Python	"BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions" [paper] [data]
2024-07	arXiv	CodeUpdateArena	670	Python	"CodeUpdateArena: Benchmarking Knowledge Editing on API Updates" [paper] [data]
2024-07	arXiv	LBPP	161	Python	"On Leakage of Code Generation Evaluation Datasets" [paper] [data]
2024-07	arXiv	NoviCode	150	Python	"NoviCode: Generating Programs from Natural Language Utterances by Novices" [paper] [data]
2024-07	arXiv	Case2Code	1.3M	Python	"Case2Code: Learning Inductive Reasoning with Synthetic Data" [paper] [data]
2024-07	arXiv	SciCode	338	Python	"SciCode: A Research Coding Benchmark Curated by Scientists" [paper] [data]
2024-07	arXiv	auto-regression	460	Python	"Generating Unseen Code Tests In Infinitum" [paper]
2024-07	arXiv	WebApp1K	1000	JavaScript	"WebApp1K: A Practical Code-Generation Benchmark for Web App Development" [paper] [data]
2024-08	ACL 2024 Findings	CodeInsight	3409	Python	"CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow" [paper] [data]
2024-08	arXiv	DomainEval	2454	Python	"DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation" [paper] [data]
2024-09	arXiv	ComplexCodeEval	7184/3897	Python/Java	"ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code" [paper] [data]
2024-09	ASE 2024	CoCoNote	58221	Python Notebook	"Contextualized Data-Wrangling Code Generation in Computational Notebooks" [paper] [data]
2024-10	arXiv	unnamed	77	Python	"Evaluation of Code LLMs on Geospatial Code Generation" [paper] [data]

* Automatically mined/human-annotated

$^\dagger$ 1749 prompts for 48 problems

$^\ddagger$ 10538 prompts for 1420 problems

$^\diamond$ Machine/human prompts

Date	Venue	Benchmark	Size	Language	Source
2024-04	arXiv	MMCode	3548	Python	"MMCode: Evaluating Multi-Modal Code Large Language Models with Visually Rich Programming Problems" [paper] [data]
2024-05	arXiv	Plot2Code	132	Python	"Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots" [paper] [data]
2024-06	arXiv	ChartMimic	1000	Python	"ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation" [paper] [data]

Date	Venue	Benchmark	Size	Language	Source
2021-09	EMNLP 2021 Findings	CodeQA	120K/70K	Java/Python	"CodeQA: A Question Answering Dataset for Source Code Comprehension" [paper] [data]
2022-10	NAACL 2022	CS1QA	9237	Python	"CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course" [paper] [data]
2023-09	arXiv	CodeApex	250	C++	"CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models" [paper] [data]
2024-01	ICML 2024	CRUXEval	800	Python	"CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution" [paper] [data]
2024-05	arXiv	PythonIO	2650	Python	"Multiple-Choice Questions are Efficient and Robust LLM Evaluators" [paper] [data]
2024-05	arXiv	StaCCQA	270K	Python	"Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering" [paper] [data]
2024-06	arXiv	RepoQA	500	Python, C++, Java, Rust, TypeScript	"RepoQA: Evaluating Long Context Code Understanding" [paper] [data]
2024-08	arXiv	CruxEval-X	12.6K	19	"CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution" [paper] [data]
2024-09	arXiv	SpecEval	204	Java	"SpecEval: Evaluating Code Comprehension in Large Language Models via Program Specifications" [paper] [data]
2024-10	arXiv	CodeMMLU	19912	13	"CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities of CodeLLMs" [paper] [data]

• "Deep learning driven natural languages text to SQL query conversion: A survey", 2022-08, arXiv, [paper]
• "Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect", 2022-08, COLING 2022, [paper]
• "A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions", 2022-08, arXiv, [paper]
• "A survey on deep learning approaches for text-to-SQL", 2023-01, VLDB J., [paper]

Date	Venue	Benchmark	Size	Language	Source
2017-08	arXiv	WikiSQL	80654		"Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning" [paper] [data]
2018-06	CL 2018	Advising	4570		"Improving Text-to-SQL Evaluation Methodology" [paper] [data]
2018-09	EMNLP 2018	Spider	10181		"Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task" [paper] [data]
2019-06	ACL 2019	SParC	12726		"SParC: Cross-Domain Semantic Parsing in Context" [paper] [data]
2019-07	WWW 2020	MIMICSQL	10000		"Text-to-SQL Generation for Question Answering on Electronic Medical Records" [paper] [data]
2019-09	EMNLP-IJCNLP 2019	CoSQL	15598		"CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases" [paper] [data]
2020-05	LREC 2020	Criteria-to-SQL	2003		"Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing" [paper] [data]
2020-10	EMNLP 2020 Findings	Squall	11276		"On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries" [paper] [data]
2020-10	NAACL-HLT 2021	Spider-Realistic	508		"Structure-Grounded Pretraining for Text-to-SQL" [paper] [data]
2021-06	ACL/IJCNLP 2021	Spider-Syn	8034		"Towards Robustness of Text-to-SQL Models against Synonym Substitution" [paper] [data]
2021-06	NLP4Prog 2021	SEDE	12023		"Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data" [paper] [data]
2021-06	ACL/IJCNLP 2021	KaggleDBQA	400		"KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers" [paper] [data]
2021-09	EMNLP	Spider-DK	535		"Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization" [paper] [data]
2022-05	NAACL 2022 Findings	Spider-SS/CG	8034/45599		"Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment" [paper] [data]
2023-05	arXiv	BIRD	12751		"Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs" [paper] [data]
2023-06	ACL 2023	XSemPLR	24.4K		"XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations" [paper] [data]
2024-05	ACL 2024 Findings	EHR-SeqSQL	31669		"EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records" [paper]
2024-06	NAACL 2024	BookSQL	100K		"BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain" [paper] [data]
2024-08	ACL 2024 Findings	MultiSQL	9257		"MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations" [paper] [data]
2024-09	arXiv	BEAVER	93		"BEAVER: An Enterprise Benchmark for Text-to-SQL" [paper]

Date	Venue	Benchmark	Size	Language	Source
2020-06	NeurIPS 2020	Transcoder GeeksforGeeks	1.4K	C++, Java, Python	"Unsupervised Translation of Programming Languages" [paper] [data]
2021-02	NeurIPS Datasets and Benchmarks 2021	CodeTrans	11.8K	Java, C#	"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [paper] [data]
2021-08	ACL 2023 Findings	Avatar	9515	Java, Python	"AVATAR: A Parallel Corpus for Java-Python Program Translation" [paper] [data]
2022-06	AAAI 2022	CoST	132K	C++, Java, Python, C#, JS, PHP, C	"Multilingual Code Snippets Training for Program Translation" [paper] [data]
2022-06	arXiv	XLCoST	567K	C++, Java, Python, C#, JS, PHP, C	"XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence" [paper] [data]
2023-03	arXiv	xCodeEval	5.6M	C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust	"xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [paper] [data]
2023-03	arXiv	HumanEval-X	1640	Python, C++, Java, JS, Go	"CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [paper] [data]
2023-08	arXiv	G-TransEval	4000	C++, Java, C#, JS, Python	"On the Evaluation of Neural Code Translation: Taxonomy and Benchmark" [paper] [data]
2023-10	arXiv	CodeTransOcean	270.5K	45	"CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation" [paper] [data]

• "Neural Program Repair: Systems, Challenges and Solutions", 2022-02, Internetware 2022, [paper]
• "A Survey of Learning-based Automated Program Repair", 2023-01, arXiv, [paper]
• "A Survey on Automated Program Repair Techniques", 2023-03, arXiv, [paper]

Date	Venue	Benchmark	Size	Language	Source
2014-07	ISSTA 2014	Defects4J	357	Java	"Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs" [paper] [data]
2015-12	IEEE Trans. Software Engineering	ManyBugs/IntroClass	185/998	C	"The ManyBugs and IntroClass Benchmarks for Automated Repair of C Programs" [paper] [data]
2016-11	FSE 2016	BugAID	105K	JS	"Discovering Bug Patterns in JavaScript" [paper] [data]
2017-02	AAAI 2017	DeepFix	6971	C	"DeepFix: Fixing Common C Language Errors by Deep Learning" [paper] [data]
2017-05	ICSE-C 2017	Codeflaws	3902	C	"DeepFix: Fixing Common C Language Errors by Deep Learning" [paper] [data]
2017-10	SPLASH 2017	QuixBugs	80	Java, Python	"QuixBugs: a multi-lingual program repair benchmark set based on the quixey challenge" [paper] [data]
2018-05	MSR 2018	Bugs.jar	1158	Java	"Bugs.jar: a large-scale, diverse dataset of real-world Java bugs" [paper] [data]
2018-12	ACM Trans. Softw. Eng. Methodol.	BFP	124K	Java	"An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation" [paper] [data]
2019-01	SANER 2019	Bears	251	Java	"Bears: An Extensible Java Bug Benchmark for Automatic Program Repair Studies" [paper] [data]
2019-01	ICSE 2019	unnamed	21.8K *	Java	"On Learning Meaningful Code Changes via Neural Machine Translation" [paper] [data]
2019-04	ICST 2019	BugsJS	453	JS	"BugsJS: a Benchmark of JavaScript Bugs" [paper] [data]
2019-05	ICSE 2019	BugSwarm	1827/1264	Java/Python	"BugSwarm: mining and continuously growing a dataset of reproducible failures and fixes" [paper] [data]
2019-05	ICSE 2019	CPatMiner	17K *	Java	"Graph-based mining of in-the-wild, fine-grained, semantic code change patterns" [paper] [data]
2019-05	MSR 2020	ManySStuBs4J	154K	Java	"How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset" [paper] [data]
2019-11	ASE 2019	Refactory	1783	Python	"Re-factoring based program repair applied to programming assignments" [paper] [data]
2020-07	ISSTA 2020	CoCoNut	24M	Java, Python, C, JS	"CoCoNuT: combining context-aware neural translation models using ensemble for program repair" [paper] [data]
2020-10	Inf. Softw. Technol.	Review4Repair	58021	Java	"Review4Repair: Code Review Aided Automatic Program Repairing" [paper] [data]
2020-11	ESEC/FSE 2020	BugsInPy	493	Python	"BugsInPy: A Database of Existing Bugs in Python Programs to Enable Controlled Testing and Debugging Studies" [paper] [data]
2021-07	ICML 2021	TFix	105K	JS	"TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer" [paper] [data]
2021-08	arXiv	Megadiff	663K *	Java	"Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size" [paper] [data]
2022-01	SSB/TSSB	MSR 2022	9M/3M	Python	"TSSB-3M: Mining single statement bugs at massive scale" [paper] [data]
2022-10	MSR 2022	FixJS	324K	JS	"FixJS: a dataset of bug-fixing JavaScript commits" [paper] [data]
2022-11	ESEC/FSE 2022	TypeBugs	93	Python	"PyTER: Effective Program Repair for Python Type Errors" [paper] [data]
2023-03	arXiv	xCodeEval	4.7M	C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust	"xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [paper] [data]
2023-04	arXiv	RunBugRun	450K	C, C++, Java, Python, JS, Ruby, Go, PHP	"RunBugRun -- An Executable Dataset for Automated Program Repair" [paper] [data]
2023-08	arXiv	HumanEvalPack	984	Python, JS, Go, Java, C++, Rust	"OctoPack: Instruction Tuning Code Large Language Models" [paper] [data]
2024-01	arXiv	DebugBench	4253	C++, Java, Python	"DebugBench: Evaluating Debugging Capability of Large Language Models" [paper] [data]

* These are code-change datasest, and only a subset therein concerns bug fixing.

• "A Survey of Automatic Source Code Summarization", 2022-02, Symmetry, [paper]

Date	Venue	Benchmark	Size	Language	Source
2016-08	ACL 2016	CODE-NN	66K/32K	C#/SQL	"Summarizing Source Code using a Neural Attention Model" [paper] [data]
2017-07	IJCNLP 2017	unnamed	150K	Python	"A parallel corpus of Python functions and documentation strings for automated code documentation and code generation" [paper] [data]
2018-05	ICPC 2018	DeepCom	588K	Java	"Deep code comment generation" [paper] [data]
2018-07	IJCAI 2018	TL-CodeSum	411K	Java	"Summarizing Source Code with Transferred API Knowledge" [paper] [data]
2018-11	ASE 2018	unnamed	109K	Python	"Improving Automatic Source Code Summarization via Deep Reinforcement Learning" [paper] [data]
2019-09	arxiv	CodeSearchNet	2.3M	Go, JS, Python, PHP, Java, Ruby	"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [paper] [data]
2023-08	arXiv	HumanEvalPack	984	Python, JS, Go, Java, C++, Rust	"OctoPack: Instruction Tuning Code Large Language Models" [paper] [data]

• "Benchmarking Software Vulnerability Detection Techniques: A Survey", 2023-03, arXiv, [paper]

Date	Venue	Benchmark	Size	Language	Source
2018-01	NDSS 2018	CGD	62K	C, C++	"VulDeePecker: A Deep Learning-Based System for Vulnerability Detection" [paper] [data]
2018-04	IEEE Trans. Ind. Informatics	unnamed	32988	C, C++	"Cross-Project Transfer Representation Learning for Vulnerable Function Discovery" [paper] [data]
2018-07	ICMLA 2018	Draper VDISC	12.8M	C, C++	"Automated Vulnerability Detection in Source Code Using Deep Representation Learning" [paper] [data]
2018-07	IEEE TDSC	SySeVR	15591	C, C++	"SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities" [paper] [data]
2019-02	MSR 2019	unnamed	624	Java	"A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software" [paper] [data]
2019-09	NeurIPS 2019	Devign	49K	C	"Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks" [paper] [data]
2019-11	IEEE TDSC	unnamed	170K	C, C++	"Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases" [paper] [data]
2019-12	ICLR 2020	GREAT	2.8M	Python	"Global Relational Models of Source Code" [paper] [data]
2020-01	IEEE TDSC	MVD	182K	C, C++	"μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection" [paper] [data]
2020-02	ICICS 2019	unnamed	1471	C	"Deep Learning-Based Vulnerable Function Detection: A Benchmark" [paper] [data]
2020-09	IEEE Trans. Software Eng.	ReVeal	18K	C	"Deep Learning based Vulnerability Detection: Are We There Yet?" [paper] [data]
2020-09	MSR 2020	Big-Vul	265K	C, C++	"A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries" [paper] [data]
2021-02	ICSE (SEIP) 2021	D2A	1.3M	C, C++	"D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis" [paper] [data]
2021-05	NeurIPS 2021	PyPIBugs	2374	Python	"Self-Supervised Bug Detection and Repair" [paper] [data]
2021-07	In PROMISE 2021	CVEfixes	5495	27	"CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software" [paper] [data]
2021-08	ESEC/FSE 2021	CrossVul	27476	40+	"CrossVul: a cross-language vulnerability dataset with commit data" [paper] [data]
2023-04	RAID 2023	DiverseVul	349K	C, C++	"DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection" [paper] [data]
2023-06	arXiv	VulnPatchPairs	26K	C	"Limits of Machine Learning for Automatic Vulnerability Detection" [paper] [data]
2023-11	arXiv	VulBench	455	C	"How Far Have We Gone in Vulnerability Detection Using Large Language Models" [paper] [data]
2024-03	arXiv	PrimeVul	236K	C/C++	"Vulnerability Detection with Code Language Models: How Far Are We?" [paper]
2024-06	arXiv	VulDetectBench	1000	C/C++	"VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models" [paper] [data]
2024-08	arXiv	CodeJudge-Eval	1860	Python	"CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?" [paper] [data]

• "Code Search: A Survey of Techniques for Finding Code", 2022-04, ICSME 2021, [[paper](ACM Comput. Surv)]
• "A Survey of Deep Code Search", 2023-05, arXiv, [paper]

Date	Venue	Benchmark	Size	Language	Source
2018-03	WWW 2018	StaQC	148K/120K	Python/SQL	"StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow" [paper] [data]
2018-05	ICSE 2018	DeepCS	16.2M	Java	"Deep Code Search" [paper] [data]
2018-05	MSR 2018	CoNaLa	600K/2.9K	Python	"Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow" [paper] [data]
2019-08	arXiv	unnamed	287	Java	"Neural Code Search Evaluation Dataset" [paper] [data]
2019-09	arXiv	CodeSearchNet	2.3M/99	Go, PHP, JS, Python, Java, Ruby	"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [paper] [data]
2020-02	SANER 2020	CosBench	52	Java	"Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries" [paper] [data]
2020-08	arXiv	SO-DS	2.2K	Python	"Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent" [paper] [data]
2020-10	ACM Trans. Knowl. Discov. Data	FB-Java	249K	Java	"Deep Graph Matching and Searching for Semantic Code Retrieval" [paper] [data]
2021-02	NeurIPS Datasets and Benchmarks 2021	AdvTest/WebQueryTest	280K/1K	Python	"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [paper] [[data]]
2021-05	ACL/IJCNLP 2021	CoSQA	21K	Python	"CoSQA: 20,000+ Web Queries for Code Search and Question Answering" [paper] [data]
2024-03	arXiv	ProCQA	5.2M	C, C++, Java, Python, Ruby, Lisp, JS, C#, Go, Rust, PHP	"ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search" [paper] [data]
2024-06	arXiv	CoSQA+	109K	Python	"CoSQA+: Enhancing Code Search Dataset with Matching Code" [paper] [data]
2024-07	arXiv	CoIR	~2M	14	"CoIR: A Comprehensive Benchmark for Code Information Retrieval Models" [paper] [data]
2024-08	arXiv	SeqCoBench	14.5K	Python	"What can Large Language Models Capture about Code Functional Equivalence?" [paper]

Date	Venue	Benchmark	Size	Language	Source
2019-12	ESEC/FSE 2020	TypeWriter OSS	208K	Python	"TypeWriter: Neural Type Prediction with Search-based Validation" [paper] [data]
2020-04	PLDI 2020	Typilus	252K	Python	"Typilus: Neural Type Hints" [paper] [data]
2020-04	ICLR 2020	LambdaNet	300 *	TypeScript	"LambdaNet: Probabilistic Type Inference using Graph Neural Networks" [paper] [data]
2021-04	MSR 2021	ManyTypes4Py	869K	Python	"ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference" [paper] [data]
2022-10	MSR 2022	ManyTypes4TypeScript	9.1M	TypeScript	"ManyTypes4TypeScript: a comprehensive TypeScript dataset for sequence-based type inference" [paper] [data]
2023-02	ECOOP 2023	TypeWeaver	513 *	TypeScript	"Do Machine Learning Models Produce TypeScript Types That Type Check?" [paper] [data]
2023-03	ICLR 2023	BetterTypes4Py/InferTypes4Py	608K/4.6K	Python	"TypeT5: Seq2seq Type Inference using Static Analysis" [paper] [data]
2023-05	arXiv	OpenTau	744 *	TypeScript	"Type Prediction With Program Decomposition and Fill-in-the-Type Training" [paper] [data]

* These are project counts.

• "On the Evaluation of Commit Message Generation Models: An Experimental Study", 2021-07, ICSME 2021, [paper]

Date	Venue	Benchmark	Size	Language	Source
2017-03	ICPC 2017	unnamed	509K	Java	"Towards Automatic Generation of Short Summaries of Commits" [paper] [data]
2017-04	ACL 2017	CommitGen	153K	Python, JS, C++, Java	"A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes" [paper] [data]
2017-08	ASE 2017	CommitGen	32K/75K *	Java	"Automatically Generating Commit Messages from Diffs using Neural Machine Translation" [paper] [data]
2018-09	ASE 2018	NNGen	27K	Java	"Neural-machine-translation-based commit message generation: how far are we?" [paper] [data]
2019-05	MSR 2019	PtrGNCMsg	64.9K	Java	"Generating commit messages from diffs using pointer-generator network" [paper] [[data(https://zenodo.org/records/2593787)]]
2019-08	IJCAI 2019	CoDiSum	90.7K	Java	"Commit message generation for source code changes" [paper] [data]
2019-12	IEEE Trans. Software Eng.	ATOM	160K	Java	"ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking" [paper] [data]
2021-05	arXiv	CommitBERT	346K	Python, PHP, Go, Java, JS, Ruby	"CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model" [paper] [data]
2021-07	ICSME 2021	MCMD	2.25M	Java, C#, C++, Python, JS	"On the Evaluation of Commit Message Generation Models: An Experimental Study" [paper] [data]
2021-07	ACM Trans. Softw. Eng. Methodol.	CoRec	107K	Java	"Context-aware Retrieval-based Deep Commit Message Generation" [paper] [data]
2023-07	ASE 2023	ExGroFi	19263	Java	"Delving into Commit-Issue Correlation to Enhance Commit Message Generation Models" [paper] [data]
2023-08	ASE 2023	CommitChronicle	10.7M	20	"From Commit Message Generation to History-Aware Commit Message Completion" [paper] [data]

* with/without verb-direct object filter

Date	Venue	Benchmark	Size	Language	Source
2023-03	arXiv	RepoEval	1600/1600/373 *	Python	"RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation" [paper] [data]
2023-06	ICLR 2024	RepoBench	890K/9M/43K $^\dagger$	Python, Java	"RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems" [paper] [data]
2023-06	NeurIPS 2023	PragmaticCode	880 **	Java	"Guiding Language Models of Code with Global Context using Monitors" [paper] [data]
2023-06	arXiv	Stack-Repo	816K	Java	"RepoFusion: Training Code Models to Understand Your Repository" [paper] [data]
2023-09	ISMB 2024	BioCoder	2269/460/460	Python, Java	"BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models" [paper] [data]
2023-09	arXiv	CodePlan	645/21 $^\ddagger$	C#/Python $^\ddagger$	"CodePlan: Repository-level Coding using LLMs and Planning" [paper] [data]
2023-10	arXiv	SWE-Bench	2294	Python	"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" [paper] [data]
2023-10	arXiv	CrossCodeEval	9928	Python, Java, TypeScript, C#	"CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion" [paper] [data]
2024-03	arXiv	EvoCodeBench	275	Python	"EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories" [paper] [data]
2024-05	ACL 2024 Findings	DevEval	1874	Python	"DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories" [paper] [data]
2024-06	arXiv	JavaBench	389	Java	"Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" [paper] [data]
2024-06	arXiv	HumanEvo	200/200	Python/Java	"Towards more realistic evaluation of LLM-based code generation: an experimental study and beyond" [paper] [data]
2024-06	arXiv	RepoExec	355	Python	"REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" [paper]
2024-06	arXiv	RES-Q	100	Python, JavaScript	"RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale" [paper] [data]
2024-08	arXiv	SWE-bench-java	91	Java	"SWE-bench-java: A GitHub Issue Resolving Benchmark for Java" [paper] [data]
2024-10	arXiv	Codev-Bench	296	Python	"Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?" [paper] [data]
2024-10	arXiv	SWE-bench M	617	JavaScript	"SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?" [paper] [data]
2024-10	arXiv	SWE-Bench+	548	Python	"SWE-Bench+: Enhanced Coding Benchmark for LLMs" [paper] [data]
2024-10	arXiv	DA-Code	500	Python, Bash, SQL	"DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models" [paper] [data]

*Line Completion/API Invocation Completion/Function Completion

$^\dagger$ Retrieval/Completion/Pipeline

** File count

$^\ddagger$ Migration/Temporal Edit

30 papers as a primer on LLM.

Date	Keyword	Paper	TL;DR
2014-09	Attention	Neural Machine Translation by Jointly Learning to Align and Translate	The original attention, proposed for encoder-decoder RNN
2015-08	BPE	Neural Machine Translation of Rare Words with Subword Units	Byte-pair encoding: split rare words into subword units
2017-06	Transformer	Attention Is All You Need	Replace LSTM with self-attention for long-range dependency and parallel training
2017-10	Mixed Precision Training	Mixed Precision Training	Store model weights in fp16 to save memory
2018-04	GLUE	GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding	A language understanding benchmark
2018-06	GPT	Improving Language Understanding by Generative Pre-Training	Pretraining-finetuning paradigm applied to Transformer decoder
2018-10	BERT	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	Masked Language Modeling (MLM) applied to Transformer encoder for pretraining
2019-02	GPT-2	Language Models are Unsupervised Multitask Learners	GPT made larger (1.5B). They found language models implicitly learn about downstream tasks (such as translation) during pretraining.
2019-05	SuperGLUE	SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems	Another language understanding benchmark
2019-07	RoBERTa	RoBERTa: A Robustly Optimized BERT Pretraining Approach	An optimized BERT
2019-09	Megatron-LM	Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism	Model parallelism
2019-10	ZeRO	ZeRO: Memory Optimizations Toward Training Trillion Parameter Models	Memory-efficient distributed optimization
2019-10	T5	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	Transformer encoder-decoder pretrained with an MLM-like denoising objective
2020-05	GPT-3	Language Models are Few-Shot Learners	By training an even larger version of GPT-2 (175B), they discovered a new learning paradigm: In-Context Learning (ICL)
2020-09	MMLU	Measuring Massive Multitask Language Understanding	A world-knowledge and complex reasoning benchmark
2020-12	Pile	The Pile: An 800GB Dataset of Diverse Text for Language Modeling	A diverse pretraining dataset
2021-06	LoRA	LoRA: Low-Rank Adaptation of Large Language Models	Memory-efficient finetuning
2021-09	FLAN	Finetuned Language Models Are Zero-Shot Learners	Instruction-finetuning
2021-10	T0	Multitask Prompted Training Enables Zero-Shot Task Generalization	Also instruction finetuning, but applied to the much smaller T5
2021-12	Gopher	Scaling Language Models: Methods, Analysis & Insights from Training Gopher	A 280B LLM with comprehensive experiments
2022-01	CoT	Chain-of-Thought Prompting Elicits Reasoning in Large Language Models	Chain-of-Though reasoning
2022-03	InstructGPT	Training language models to follow instructions with human feedback	GPT-3 instruction finetuned with RLHF (reinforcement learning from human feedback)
2022-03	Chinchilla	Training Compute-Optimal Large Language Models	A smaller (70B) version of Gopher that's pretrained on more data
2022-04	PaLM	PaLM: Scaling Language Modeling with Pathways	The largest dense model ever (540B)
2022-05	0-shot CoT	Large Language Models are Zero-Shot Reasoners	Tell LLMs to think step by step, and they can actually do it
2022-06	BIG Bench	Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models	Another world-knowledge and complex reasoning benchmark
2022-06	Emergent Ability	Emergent Abilities of Large Language Models	A review on emergent abilities
2022-10	Flan	Scaling Instruction-Finetuned Language Models	Consolidate all the existing instruction tuning datasets, and you get SOTA
2022-11	BLOOM	BLOOM: A 176B-Parameter Open-Access Multilingual Language Model	The largest open-source LLM, trained on 46 languages, with detailed discussion about training and evaluation
2022-12	Self-Instruct	Self-Instruct: Aligning Language Models with Self-Generated Instructions	Instruction tuning using LLM-generated data

This list aims to provide the essential background for understanding current LLM technologies, and thus excludes more recent models such as LLaMA, GPT-4 or PaLM 2. For comprehensive reviews on these more general topics, we refer to other sources such as this paper or these repositories: Awesome-LLM, Awesome AIGC Tutorials. And for LLM applications in other specific domains: Awesome Domain LLM, Awesome Tool Learning, Awesome-LLM-MT, Awesome Education LLM.

If you find this repo or our survey helpful, please consider citing us:

@article{zhang2024unifying,
   title={Unifying the Perspectives of {NLP} and Software Engineering: A Survey on Language Models for Code},
   author={Ziyin Zhang and Chaoyu Chen and Bingchang Liu and Cong Liao and Zi Gong and Hang Yu and Jianguo Li and Rui Wang},
   journal={Transactions on Machine Learning Research},
   issn={2835-8856},
   year={2024},
   url={https://openreview.net/forum?id=hkNnGqZnpa},
   note={}
}

English version

We are the AI Native team within the Platform Technology Business Group at Ant Group, dedicated to the intelligentization of Ant Group's platform engineering. Established for over three years, our team has played a pivotal role in supporting the intelligent operation and maintenance of Ant Group's cloud computing infrastructure. Our mission is to build algorithm services and platforms with a wide user base through world-class technological innovation and impact, supporting the implementation of internal and external products and businesses. Embracing an innovation-driven ethos, our team not only supports business implementation but also propels technological influence. Over the past three years, we have published more than 20 papers at top conferences like ICLR, NeurIPS, KDD, and ACL. Our innovative business outcomes have earned us two Ant Technology's highest T-Star awards and one SuperMA award from Ant Group. Our open-source project CodeFuse has received 4K stars as of February 2024, and our models have been downloaded over 1.5 million times on Huggingface and Modelscope.

We are on the lookout for top talents to join our vibrant team! If you're eager to develop your career in an environment filled with energy, innovation, and a culture of excellence, we welcome you to explore our career opportunities for both campus and experienced hires. Join us and be a part of creating the next milestone in the industry.

Campus Recruitment: https://hrrecommend.antgroup.com/guide.html?code=8uoP5mlus5DqQYbE_EnqcE2FD5JZH21MwvMUIb9mb6X3osXPuBraG54SyM8GLn_7

Experienced Hires: https://talent.antgroup.com/off-campus-position?positionId=1933830

中文版

我们是平台技术事业群 AI Native 团队，负责蚂蚁蚂蚁集团平台工程的智能化，团队成立 3 年多以来，支持了蚂蚁集团云计算基础设施智能化运维的升级改造。团队的 Mission 是，通过世界级的技术创新和影响，构建有广泛用户的算法服务和平台，支撑内外部产品和业务落地。团队秉承创新基因，在支撑业务落地的同时，推动技术影响。3 年以来在 ICLR、NeurIPS、KDD、ACL 等顶会发表论文 20 余篇，创新业务结果获得两次蚂蚁技术最高奖 T-Star，1 次蚂蚁集团最高奖 SuperMA。开源项目 CodeFuse 获得 4K 点赞(2024 年 2 月)，Huggingface 和 modelscope 上模型累积下载量超过 150 万次。

我们正在寻找行业中的佼佼者加入我们的团队！如果您希望在一个充满活力、创新和卓越文化的环境中发展您的职业生涯，欢迎您查看我们的社招&校招机会，加入我们，一起创造下一个行业里程碑。

校招：https://hrrecommend.antgroup.com/guide.html?code=8uoP5mlus5DqQYbE_EnqcE2FD5JZH21MwvMUIb9mb6X3osXPuBraG54SyM8GLn_7

社招：https://talent.antgroup.com/off-campus-position?positionId=1933830

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