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Title: GitHub - DeepSoftwareAnalytics/Awesome-Issue-Resolution: Advances and Frontiers of LLM-based Issue Resolution in Software Engineering A Comprehensive Survey

Open Graph Title: GitHub - DeepSoftwareAnalytics/Awesome-Issue-Resolution: Advances and Frontiers of LLM-based Issue Resolution in Software Engineering A Comprehensive Survey

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Description: Advances and Frontiers of LLM-based Issue Resolution in Software Engineering A Comprehensive Survey - DeepSoftwareAnalytics/Awesome-Issue-Resolution

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X Description: Advances and Frontiers of LLM-based Issue Resolution in Software Engineering A Comprehensive Survey - DeepSoftwareAnalytics/Awesome-Issue-Resolution

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https://github.com/sindresorhus/awesome
https://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/paper/
https://arxiv.org/abs/2601.11655
https://huggingface.co/papers/2601.11655
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๐Ÿ“– Documentation Websitehttps://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/
๐Ÿ“„ Full Paperhttps://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/paper/
๐Ÿ“‹ Tables & Resourceshttps://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/tables/
https://huggingface.co/papers/2601.11655https://huggingface.co/papers/2601.11655
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https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution/issues
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution/blob/main/docs/images/awesome-issue-resolution.png
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-abstract
Datahttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-data
๐Ÿ“Š Evaluation Datasetshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-evaluation-datasets
๐ŸŽฏ Training Datasetshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-training-datasets
๐Ÿ“ฅ Data Collection Methodshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-data-collection
๐Ÿ”ฌ Data Synthesis Methodshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-data-synthesis
Methodshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#%EF%B8%8F-methods
๐Ÿค– Single-Agent Systemshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-single-agent-systems
๐Ÿ‘ฅ Multi-Agent Systemshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-multi-agent-systems
๐Ÿ”„ Workflow-Based Methodshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-workflow-based-methods
๐Ÿ› ๏ธ Tool-Augmented Methodshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#%EF%B8%8F-tool-augmented-methods
๐Ÿง  Memory-Enhanced Methodshttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-memory-enhanced-methods
โšก Inference-Time Scalinghttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-inference-time-scaling
๐Ÿ“š Supervised Fine-Tuning (SFT)https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-supervised-fine-tuning-sft
๐ŸŽฎ Reinforcement Learning (RL)https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-reinforcement-learning-rl
Analysishttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-analysis
๐Ÿ“ˆ Data Analysishttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-data-analysis
๐Ÿ” Methods Analysishttps://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-methods-analysis
Tables & Resourceshttps://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/tables/
Full Paperhttps://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/paper/
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https://aclanthology.org/2025.findings-acl.384/
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https://arxiv.org/abs/2510.21903
https://arxiv.org/abs/2512.10398
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-multi-agent-systems
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http://dx.doi.org/10.1145/3650212.3680384
https://arxiv.org/abs/2406.01304
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https://arxiv.org/abs/2409.00899
https://github.com/lingxi-agent/Lingxi/blob/master/docs/Lingxi%20Technical%20Report%202505.pdf
https://devlo.ai/blog/devlo-swe-bench-sota/
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https://arxiv.org/abs/2409.16299
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https://arxiv.org/abs/2507.06229
https://arxiv.org/abs/2507.23361
https://arxiv.org/abs/2508.02611
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-workflow-based-methods
https://api.semanticscholar.org/CorpusID:277850376
https://arxiv.org/abs/2410.04485
https://aclanthology.org/2025.findings-acl.252/
http://dx.doi.org/10.18653/v1/2025.findings-acl.252
https://aclanthology.org/2025.findings-acl.384/
http://dx.doi.org/10.18653/v1/2025.findings-acl.384
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#๏ธ-tool-augmented-methods
https://openreview.net/forum?id=qevq3FZ63J
http://dx.doi.org/10.1145/3650212.3680384
http://dx.doi.org/10.1145/3696630.3728549
https://openreview.net/forum?id=OJd3ayDDoF
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https://arxiv.org/abs/2409.11190
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https://arxiv.org/abs/2506.15655
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https://arxiv.org/abs/2511.16004
https://arxiv.org/abs/2512.22469
https://arxiv.org/abs/2601.13713
https://arxiv.org/abs/2602.02084
https://ayanami2003.github.io/RPG-Encoder/
https://github.com/microsoft/RPG-ZeroRepo
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-memory-enhanced-methods
https://arxiv.org/abs/2411.01114
https://arxiv.org/abs/2411.13941
https://openreview.net/forum?id=3UKOzGWCVY
https://arxiv.org/abs/2505.22954
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https://arxiv.org/abs/2601.06789
https://arxiv.org/abs/2601.06789
https://github.com/QuantaAlpha/MemGovern
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-supervised-fine-tuning-sft
https://arxiv.org/abs/2412.19031
https://nebius.com/blog/posts/scaling-data-collection-for-training-swe-agents
https://openreview.net/forum?id=z3lG70Azbg
https://arxiv.org/abs/2503.23803
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https://arxiv.org/abs/2505.18955
https://arxiv.org/abs/2506.12728
https://www.notion.so/SWE-Swiss-A-Multi-Task-Fine-Tuning-and-RL-Recipe-for-High-Performance-Issue-Resolution-21e174dedd4880ea829ed4c861c44f88
https://arxiv.org/abs/2509.25193
https://arxiv.org/abs/2509.23045
https://arxiv.org/abs/2512.22087
https://arxiv.org/abs/2601.01426
https://arxiv.org/abs/2601.04171
https://arxiv.org/abs/2505.16901
https://github.com/codefuse-ai/CodeFuse-CGM
https://huggingface.co/codefuse-ai/CodeFuse-CGM-72B
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-reinforcement-learning-rl
https://openreview.net/forum?id=ULblO61XZ0
https://aclanthology.org/2025.acl-long.559/
https://arxiv.org/abs/2505.16975
https://arxiv.org/abs/2505.23604
https://arxiv.org/abs/2506.11425
https://pretty-radio-b75.notion.site/DeepSWE-Training-a-Fully-Open-sourced-State-of-the-Art-Coding-Agent-by-Scaling-RL-22281902c1468193aabbe9a8c59bbe33
https://arxiv.org/abs/2506.07636
https://arxiv.org/abs/2508.03012
https://www.notion.so/SWE-Swiss-A-Multi-Task-Fine-Tuning-and-RL-Recipe-for-High-Performance-Issue-Resolution-21e174dedd4880ea829ed4c861c44f88
https://arxiv.org/abs/2508.11553
https://arxiv.org/abs/2508.03501
https://arxiv.org/abs/2509.00971
https://arxiv.org/abs/2510.02387
https://arxiv.org/abs/2509.12434
https://arxiv.org/abs/2509.23045
https://arxiv.org/abs/2510.11967
https://openreview.net/forum?id=yPWJG9wgll
https://aclanthology.org/2025.emnlp-industry.109/
http://dx.doi.org/10.18653/v1/2025.emnlp-industry.109
https://arxiv.org/abs/2512.18552
https://arxiv.org/abs/2512.12216
https://arxiv.org/abs/2510.25992
https://aclanthology.org/2025.naacl-long.404/
http://dx.doi.org/10.18653/v1/2025.naacl-long.404
https://arxiv.org/abs/2512.21919
https://arxiv.org/abs/2512.20957
https://arxiv.org/abs/2512.24873
https://arxiv.org/abs/2510.18779
https://arxiv.org/abs/2504.13914
https://arxiv.org/abs/2512.02556
https://arxiv.org/abs/2507.20534
https://arxiv.org/abs/2508.10925
https://arxiv.org/abs/2505.09388
https://arxiv.org/abs/2508.06471
https://arxiv.org/abs/2506.13585
https://arxiv.org/abs/2509.18883
https://arxiv.org/abs/2601.02780
https://arxiv.org/abs/2602.03411
https://github.com/RUCAIBox/SWE-Master
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-inference-time-scaling
https://openreview.net/forum?id=G7sIFXugTX
https://arxiv.org/abs/2501.14723
https://arxiv.org/abs/2509.02360
https://arxiv.org/abs/2510.16059
https://arxiv.org/abs/2601.04171
https://scale.com/research/agenticrubrics
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-data-collection-methods
https://openreview.net/forum?id=nMpJoVmRy1
https://openreview.net/forum?id=OGWkr7gXka
https://arxiv.org/abs/2506.10954
https://arxiv.org/abs/2507.11059
https://arxiv.org/abs/2508.01550
https://arxiv.org/abs/2512.06915
https://arxiv.org/abs/2602.00592
https://huggingface.co/collections/8sj7df9k8m5x8/docksmith
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-data-synthesis-methods
https://openreview.net/forum?id=3UKOzGWCVY
https://openreview.net/forum?id=7evvwwdo3z
https://arxiv.org/abs/2504.14757
https://openreview.net/forum?id=63iVrXc8cC
https://openreview.net/forum?id=P9DQ2IExgS
https://arxiv.org/abs/2509.08724
https://arxiv.org/abs/2602.03419
https://github.com/RUCAIBox/SWE-World
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-data-analysis
https://arxiv.org/abs/2503.15223
https://arxiv.org/abs/2506.09289
https://arxiv.org/abs/2506.17812
https://arxiv.org/abs/2507.02825
https://arxiv.org/abs/2506.12286
http://dx.doi.org/10.1109/ICSE-Companion66252.2025.00075
https://arxiv.org/abs/2512.10218
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-methods-analysis
https://arxiv.org/abs/2406.04464
http://dx.doi.org/10.1109/SANER64311.2025.00068
https://arxiv.org/abs/2502.08235
https://arxiv.org/abs/2503.12374
https://arxiv.org/abs/2505.23671
https://arxiv.org/abs/2506.17208
https://arxiv.org/abs/2507.02976
https://arxiv.org/abs/2509.13941
https://arxiv.org/abs/2504.08696
https://arxiv.org/abs/2505.20182
https://insights.logicstar.ai/
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-statistical-tables
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#evaluation--training-datasets
https://github.com/InternLM/SWE-Fixer
https://huggingface.co/datasets/internlm/SWE-Fixer-Train-110K
https://huggingface.co/datasets/internlm/SWE-Fixer-Eval
https://github.com/SWE-bench/SWE-smith
https://huggingface.co/datasets/SWE-bench/SWE-smith
https://github.com/SWE-Lego/SWE-Lego
https://huggingface.co/SWE-Lego/datasets
https://github.com/SWE-rebench/SWE-bench-fork
https://huggingface.co/datasets/nebius/SWE-rebench
https://github.com/SWE-bench/SWE-bench
https://huggingface.co/datasets/princeton-nlp/SWE-bench/viewer/default/train
https://github.com/Hambaobao/SWE-Flow
https://github.com/R2E-Gym/R2E-Gym
https://huggingface.co/R2E-Gym/datasets
https://huggingface.co/datasets/nebius/SWE-bench-extra
https://github.com/SWE-Gym/SWE-Gym
https://huggingface.co/SWE-Gym/datasets
https://github.com/SWE-bench/SWE-bench
https://huggingface.co/datasets/princeton-nlp/SWE-bench
https://github.com/multi-swe-bench/multi-swe-bench-env
https://huggingface.co/datasets/Daoguang/Multi-SWE-bench
https://github.com/microsoft/FEA-Bench/
https://huggingface.co/datasets/microsoft/FEA-Bench
https://github.com/microsoft/SWE-bench-Live
https://huggingface.co/datasets/SWE-bench-Live/SWE-bench-Live
https://github.com/gersteinlab/LocAgent
https://huggingface.co/datasets/czlll/Loc-Bench_V1
https://github.com/SWE-bench/SWE-bench
https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified
https://github.com/SWE-bench/SWE-bench
https://huggingface.co/datasets/princeton-nlp/SWE-bench_Lite
https://github.com/MERA-Evaluation/SWE-MERA-submissions
https://huggingface.co/datasets/MERA-evaluation/SWE-MERA
https://github.com/thomasjoshi/agents-never-forget
https://github.com/microsoft/prose/tree/main/misc/SWE-Sharp-Bench
https://huggingface.co/datasets/microsoft/SWE-Sharp-Bench
https://github.com/SWE-Perf/swe-perf
https://huggingface.co/datasets/SWE-Perf/SWE-Perf
https://github.com/luolin101/CodeV
https://huggingface.co/datasets/luolin101/Visual-SWE-bench
https://github.com/bdqnghi/SWE-EVO
https://github.com/multi-swe-bench/multi-swe-bench
https://huggingface.co/datasets/ByteDance-Seed/Multi-SWE-bench
https://github.com/amazon-science/SWE-PolyBench
https://huggingface.co/datasets/AmazonScience/SWE-PolyBench
https://huggingface.co/datasets/Sellopale/SWE-PolyBench_500
https://github.com/kwaipilot/SWE-Compass/
https://huggingface.co/datasets/Kwaipilot/SWE-Compass
https://github.com/scaleapi/SWE-bench_Pro-os
https://huggingface.co/datasets/ScaleAI/SWE-bench_Pro
https://github.com/TuringEnterprises/SWE-Bench-plus-plus
https://huggingface.co/datasets/TuringEnterprises/SWE-Bench-plus-plus
https://github.com/openai/frontier-evals
https://github.com/deepsoftwareanalytics/omnigirl
https://huggingface.co/datasets/Deep-Software-Analytics/OmniGIRL
https://github.com/SWE-bench/SWE-bench
https://huggingface.co/datasets/SWE-bench/SWE-bench_Multimodal
https://github.com/swefficiency/swefficiency-site
https://github.com/DeepSoftwareAnalytics/swe-factory
https://huggingface.co/SWE-Factory
https://github.com/microsoft/SWE-bench-Live
https://huggingface.co/datasets/SWE-bench-Live/MultiLang
https://huggingface.co/datasets/SWE-bench-Live/Windows
https://github.com/SWE-bench/SWE-bench
https://huggingface.co/datasets/SWE-bench/SWE-bench_Multilingual
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#training-trajectory-datasets
https://github.com/InternLM/SWE-Fixer
https://huggingface.co/datasets/internlm/SWE-Fixer-Train-Editing-CoT-70K
https://huggingface.co/datasets/nebius/SWE-rebench-openhands-trajectories
https://github.com/R2E-Gym/R2E-Gym
https://huggingface.co/datasets/R2E-Gym/R2EGym-SFT-Trajectories
https://github.com/FSoft-AI4Code/SWE-Synth
https://huggingface.co/datasets/swesynth/SWE-Synth_Moatless-SFT-Trajectories
https://github.com/DeepSoftwareAnalytics/swe-factory
https://huggingface.co/datasets/SWE-Factory/DeepSWE-Agent-Kimi-K2-Trajectories-2.8K
https://github.com/SWE-Gym/SWE-Gym
https://huggingface.co/datasets/SWE-Gym/OpenHands-SFT-Trajectories
https://github.com/SWE-Lego/SWE-Lego
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#sft-based-methods
https://huggingface.co/datasets/nebius/SWE-rebench-openhands-trajectories
https://huggingface.co/nebius/SWE-rebench-openhands-Qwen3-235B-A22B
https://github.com/SWE-Lego/SWE-Lego
https://huggingface.co/SWE-Lego/datasets
https://huggingface.co/SWE-Lego/SWE-Lego-Qwen3-32B
https://github.com/codefuse-ai/CodeFuse-CGM
https://huggingface.co/codefuse-ai/CodeFuse-CGM-72B
https://huggingface.co/datasets/nebius/SWE-rebench-openhands-trajectories
https://huggingface.co/nebius/SWE-rebench-openhands-Qwen3-30B-A3B
https://mistral.ai/news/devstral
https://huggingface.co/mistralai/Devstral-Small-2507
https://github.com/ucsb-mlsec/Co-PatcheR
https://huggingface.co/collections/UCSB-SURFI/co-patcher
https://github.com/zhenyuhe00/SWE-Swiss
https://huggingface.co/SWE-Swiss/datasets
https://huggingface.co/SWE-Swiss/models
https://github.com/SWE-Lego/SWE-Lego
https://huggingface.co/SWE-Lego/datasets
https://huggingface.co/SWE-Lego/SWE-Lego-Qwen3-8B
https://github.com/LingmaTongyi/Lingma-SWE-GPT
https://github.com/SWE-Gym/SWE-Gym
https://huggingface.co/SWE-Gym
https://github.com/LingmaTongyi/Lingma-SWE-GPT
https://github.com/SWE-Gym/SWE-Gym
https://huggingface.co/SWE-Gym
https://github.com/SWE-Gym/SWE-Gym
https://huggingface.co/SWE-Gym
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#rl-based-methods
https://github.com/meituan-longcat/LongCat-Flash-Thinking
https://huggingface.co/meituan-longcat/LongCat-Flash-Thinking
https://github.com/MoonshotAI/Kimi-Dev
https://huggingface.co/moonshotai/Kimi-Dev-72B
https://github.com/facebookresearch/swe-rl
https://github.com/All-Hands-AI/OpenHands
https://huggingface.co/OpenHands/openhands-critic-32b-exp-20250417
https://huggingface.co/Kwaipilot/KAT-Dev
https://github.com/zhenyuhe00/SWE-Swiss
https://huggingface.co/SWE-Swiss/datasets
https://huggingface.co/SWE-Swiss/models
https://github.com/sunnweiwei/FoldAgent
https://drive.google.com/file/u/0/d/1aX5xXAN5R-gLKd8A0AY-troxXJRawyAM/view?usp=sharing&pli=1
https://github.com/Chojikun/seamlessflow
https://github.com/agentica-project/rllm
https://huggingface.co/datasets/R2E-Gym/R2E-Gym-Subset
https://huggingface.co/agentica-org/DeepSWE-Preview
https://github.com/All-Hands-AI/OpenHands
https://huggingface.co/OpenHands/openhands-lm-32b-v0.1
https://github.com/THUDM/SWE-Dev
https://huggingface.co/zai-org/SWE-Dev-32B
https://github.com/satori-reasoning/Satori-SWE
https://huggingface.co/Satori-reasoning
https://huggingface.co/Satori-reasoning
https://github.com/Chojikun/seamlessflow
https://github.com/THUDM/SWE-Dev
https://huggingface.co/zai-org/SWE-Dev-7B
https://github.com/THUDM/SWE-Dev
https://huggingface.co/zai-org/SWE-Dev-8B
https://github.com/THUDM/SWE-Dev
https://huggingface.co/zai-org/SWE-Dev-9B
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#general-foundation-models
https://github.com/XiaomiMiMo/MiMo-V2-Flash
https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash
https://www.modelscope.cn/models/Kwaipilot/KAT-Dev-72B-Exp
https://github.com/deepseek-ai/DeepSeek-V3.2-Exp
https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale
https://huggingface.co/moonshotai/Kimi-K2-Instruct
https://github.com/QwenLM/Qwen3-Coder
https://huggingface.co/collections/Qwen/qwen3-coder
https://huggingface.co/zai-org/GLM-4.6
https://github.com/openai/gpt-oss
https://huggingface.co/openai/gpt-oss-120b
https://github.com/MiniMax-AI/MiniMax-M2
https://huggingface.co/MiniMaxAI/MiniMax-M2
https://github.com/openai/gpt-oss
https://huggingface.co/openai/gpt-oss-20b
https://github.com/MiniMax-AI/MiniMax-M1
https://www.modelscope.cn/models/MiniMax/MiniMax-M1-80k
https://github.com/MiniMax-AI/MiniMax-M1
https://www.modelscope.cn/models/MiniMax/MiniMax-M1-40k/summary
https://github.com/ByteDance-Seed/Seed-Thinking-v1.5
https://github.com/meta-llama/llama-models/tree/main/models/llama4
https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
https://github.com/meta-llama/llama-models/tree/main/models/llama4
https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-quick-start
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-contributing
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-related-work
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#code-generation
HumanEvalhttps://arxiv.org/abs/2107.03374
Evaluating Large Language Models Trained on Codehttps://arxiv.org/abs/2107.03374
Program Synthesis with Large Language Modelshttps://arxiv.org/abs/2108.07732
RLCoder: Reinforcement Learning for Repository-Level Code Completionhttps://doi.org/10.1109/ICSE55347.2025.00014
Top General Performance = Top Domain Performance? DomainCodeBenchhttps://arxiv.org/abs/2412.18573
Long Code Arenahttps://arxiv.org/abs/2406.11612
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuninghttps://doi.org/10.1145/3637528.3671609
RAG or Fine-tuning? A Comparative Study on LCMs-based Code Completion in Industryhttps://doi.org/10.1145/3696630.3728549
Repoformer: Selective Retrieval for Repository-Level Code Completionhttps://openreview.net/forum?id=ZUnIGtP0Nf
CodeRAG-Benchhttps://aclanthology.org/2025.findings-naacl.176/
A Survey on Large Language Models for Code Generationhttps://doi.org/10.1145/3747588
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#automated-software-generation
Communicative Agents for Software Developmenthttps://doi.org/10.18653/v1/2024.acl-long.810
Meta Programming for Multi-Agent Collaborative Frameworkhttps://openreview.net/forum?id=VtmBAGCN7o
Repository Planning Graph for Unified and Scalable Codebase Generationhttps://arxiv.org/abs/2509.16198
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#automated-software-maintenance
AssertFliphttps://arxiv.org/abs/2507.17542
Automated Generation of Issue-Reproducing Testshttps://arxiv.org/abs/2509.01616
A Survey on Software Fault Localizationhttps://doi.org/10.1109/TSE.2016.2521368
Where should the bugs be fixed?https://doi.org/10.1145/2393596.2393616
DreamLochttps://doi.org/10.1109/TR.2021.3104728
Effective Bug Triagehttps://doi.org/10.1109/ISSRE.2014.17
BLAZEhttps://doi.org/10.1109/TSE.2025.3579574
Bridging Bug Localization and Issue Fixinghttps://arxiv.org/abs/2502.15292
Hierarchical Reward Modelinghttps://aclanthology.org/2025.findings-emnlp.966/
A Benchmark for Localizing Code and Non-Code Issueshttps://arxiv.org/abs/2509.25242
TDD-Bench Verifiedhttps://arxiv.org/abs/2412.02883
SWT-Benchhttps://proceedings.neurips.cc/paper_files/paper/2024/file/94f093b41fc2666376fb1f667fe282f3-Paper-Conference.pdf
TDFlowhttps://arxiv.org/abs/2510.23761
Is Vibe Coding Safe?https://arxiv.org/abs/2512.03262
A Survey on Automated Program Repair Techniqueshttps://arxiv.org/abs/2303.18184
A Survey of Learning-based Automated Program Repairhttps://doi.org/10.1145/3631974
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#automated-environment-setup
A Benchmark for Automated Environment Setuphttps://openreview.net/forum?id=izy1oaAOeX
On-Device Environment Setup via Online Reinforcement Learninghttps://arxiv.org/abs/2509.25455
Automated Benchmark Generation for Repository-Level Coding Taskshttps://openreview.net/forum?id=BQA7dkV3iZ
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#related-surveys
A Survey on Large Language Models for Code Generationhttps://doi.org/10.1145/3747588
Agents in software engineering: survey, landscape, and visionhttps://doi.org/10.1007/s10515-025-00544-2
A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic Systemhttps://arxiv.org/abs/2510.09721
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-citation
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#once-published-on-arxiv-or-at-a-conference-please-replace-the-entry-with-the-official-citation-information-authors-doiarxiv-id-conference-name-etc
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-acknowledgements
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#special-thanks
@chao-penghttps://github.com/chao-peng
Dr. Chao Penghttps://chao-peng.github.io
@EuniAI/awesome-code-agentshttps://github.com/EuniAI/awesome-code-agents
https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution#-contact
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