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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2511.18538 |
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| author | Yang, Jian Liu, Xianglong Lv, Weifeng Deng, Ken Guo, Shawn Jing, Lin Li, Yizhi Liu, Shark Luo, Xianzhen Luo, Yuyu Pan, Changzai Shi, Ensheng Tan, Yingshui Tao, Renshuai Wu, Jiajun Wu, Xianjie Wu, Zhenhe Zan, Daoguang Zhang, Chenchen Zhang, Wei Zhu, He Zhuo, Terry Yue Cao, Kerui Cheng, Xianfu Dong, Jun Fang, Shengjie Fei, Zhiwei Guan, Xiangyuan Guo, Qipeng Han, Zhiguang James, Joseph Luo, Tianqi Li, Renyuan Li, Yuhang Liang, Yiming Liu, Congnan Liu, Jiaheng Liu, Qian Liu, Ruitong Loakman, Tyler Meng, Xiangxin Peng, Chuang Peng, Tianhao Shi, Jiajun Tang, Mingjie Wang, Boyang Wang, Haowen Wang, Yunli Xu, Fanglin Xu, Zihan Yuan, Fei Zhang, Ge Zhang, Jiayi Zhang, Xinhao Zhou, Wangchunshu Zhu, Hualei Zhu, King Dai, Bryan Liu, Aishan Li, Zhoujun Lin, Chenghua Liu, Tianyu Peng, Chao Shen, Kai Qin, Libo Song, Shuangyong Zhan, Zizheng Zhang, Jiajun Zhang, Jie Zhang, Zhaoxiang Zheng, Bo |
| author_facet | Yang, Jian Liu, Xianglong Lv, Weifeng Deng, Ken Guo, Shawn Jing, Lin Li, Yizhi Liu, Shark Luo, Xianzhen Luo, Yuyu Pan, Changzai Shi, Ensheng Tan, Yingshui Tao, Renshuai Wu, Jiajun Wu, Xianjie Wu, Zhenhe Zan, Daoguang Zhang, Chenchen Zhang, Wei Zhu, He Zhuo, Terry Yue Cao, Kerui Cheng, Xianfu Dong, Jun Fang, Shengjie Fei, Zhiwei Guan, Xiangyuan Guo, Qipeng Han, Zhiguang James, Joseph Luo, Tianqi Li, Renyuan Li, Yuhang Liang, Yiming Liu, Congnan Liu, Jiaheng Liu, Qian Liu, Ruitong Loakman, Tyler Meng, Xiangxin Peng, Chuang Peng, Tianhao Shi, Jiajun Tang, Mingjie Wang, Boyang Wang, Haowen Wang, Yunli Xu, Fanglin Xu, Zihan Yuan, Fei Zhang, Ge Zhang, Jiayi Zhang, Xinhao Zhou, Wangchunshu Zhu, Hualei Zhu, King Dai, Bryan Liu, Aishan Li, Zhoujun Lin, Chenghua Liu, Tianyu Peng, Chao Shen, Kai Qin, Libo Song, Shuangyong Zhan, Zizheng Zhang, Jiajun Zhang, Jie Zhang, Zhaoxiang Zheng, Bo |
| contents | Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, driving commercial adoption through tools like Github Copilot (Microsoft), Cursor (Anysphere), Trae (ByteDance), and Claude Code (Anthropic). While the field has evolved dramatically from rule-based systems to Transformer-based architectures, achieving performance improvements from single-digit to over 95\% success rates on benchmarks like HumanEval. In this work, we provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs, systematically examining the complete model life cycle from data curation to post-training through advanced prompting paradigms, code pre-training, supervised fine-tuning, reinforcement learning, and autonomous coding agents. We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder), critically examining the techniques, design decisions, and trade-offs. Further, we articulate the research-practice gap between academic research (e.g., benchmarks and tasks) and real-world deployment (e.g., software-related code tasks), including code correctness, security, contextual awareness of large codebases, and integration with development workflows, and map promising research directions to practical needs. Last, we conduct a series of experiments to provide a comprehensive analysis of code pre-training, supervised fine-tuning, and reinforcement learning, covering scaling law, framework selection, hyperparameter sensitivity, model architectures, and dataset comparisons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence Yang, Jian Liu, Xianglong Lv, Weifeng Deng, Ken Guo, Shawn Jing, Lin Li, Yizhi Liu, Shark Luo, Xianzhen Luo, Yuyu Pan, Changzai Shi, Ensheng Tan, Yingshui Tao, Renshuai Wu, Jiajun Wu, Xianjie Wu, Zhenhe Zan, Daoguang Zhang, Chenchen Zhang, Wei Zhu, He Zhuo, Terry Yue Cao, Kerui Cheng, Xianfu Dong, Jun Fang, Shengjie Fei, Zhiwei Guan, Xiangyuan Guo, Qipeng Han, Zhiguang James, Joseph Luo, Tianqi Li, Renyuan Li, Yuhang Liang, Yiming Liu, Congnan Liu, Jiaheng Liu, Qian Liu, Ruitong Loakman, Tyler Meng, Xiangxin Peng, Chuang Peng, Tianhao Shi, Jiajun Tang, Mingjie Wang, Boyang Wang, Haowen Wang, Yunli Xu, Fanglin Xu, Zihan Yuan, Fei Zhang, Ge Zhang, Jiayi Zhang, Xinhao Zhou, Wangchunshu Zhu, Hualei Zhu, King Dai, Bryan Liu, Aishan Li, Zhoujun Lin, Chenghua Liu, Tianyu Peng, Chao Shen, Kai Qin, Libo Song, Shuangyong Zhan, Zizheng Zhang, Jiajun Zhang, Jie Zhang, Zhaoxiang Zheng, Bo Software Engineering Computation and Language Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, driving commercial adoption through tools like Github Copilot (Microsoft), Cursor (Anysphere), Trae (ByteDance), and Claude Code (Anthropic). While the field has evolved dramatically from rule-based systems to Transformer-based architectures, achieving performance improvements from single-digit to over 95\% success rates on benchmarks like HumanEval. In this work, we provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs, systematically examining the complete model life cycle from data curation to post-training through advanced prompting paradigms, code pre-training, supervised fine-tuning, reinforcement learning, and autonomous coding agents. We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder), critically examining the techniques, design decisions, and trade-offs. Further, we articulate the research-practice gap between academic research (e.g., benchmarks and tasks) and real-world deployment (e.g., software-related code tasks), including code correctness, security, contextual awareness of large codebases, and integration with development workflows, and map promising research directions to practical needs. Last, we conduct a series of experiments to provide a comprehensive analysis of code pre-training, supervised fine-tuning, and reinforcement learning, covering scaling law, framework selection, hyperparameter sensitivity, model architectures, and dataset comparisons. |
| title | From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence |
| topic | Software Engineering Computation and Language |
| url | https://arxiv.org/abs/2511.18538 |