---
title: "基于大语言模型智能体的代码生成综述 — 董益宏 2025"
description: "This Chinese-language survey from Peking University reviews code generation agents built on large language models (LLMs)."
authors: ["董益宏", "姜雪", "钱家如", "王天", "张克驰", "金芝", "李戈"]
year: 2025
venue: "软件学报 (Journal of Software)"
source: "https://jos.org.cn/jos/article/abstract/7593"
doi: "10.13328/j.cnki.jos.007593"
licence: "Free access (jos.org.cn), no open licence stated — readonly; © 中国科学院软件研究所 / 软件学报"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/dong-2025-code-generation-llm/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/dong-2025-code-generation-llm/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2025](https://md.research.elamur.ai/free/papers/2025/page/3/) / 基于大语言模型智能体的代码生成综述

# 基于大语言模型智能体的代码生成综述

**Authors:** 董益宏, 姜雪, 钱家如, 王天, 张克驰, 金芝, 李戈 · **Year:** 2025 · **Venue:** 软件学报 (Journal of Software) · **Licence:** Free access (jos.org.cn), no open licence stated — readonly; © 中国科学院软件研究所 / 软件学报

**Links:** [Publisher's page](https://doi.org/10.13328/j.cnki.jos.007593) · [PDF](https://www.jos.org.cn//jos/article/pdf/7593) · [DOI 10.13328/j.cnki.jos.007593](https://doi.org/10.13328/j.cnki.jos.007593)

## Summary
This Chinese-language survey from Peking University reviews code generation agents built on large language models (LLMs). Plain LLMs answer in a single pass. They cannot break tasks down on their own, interact with real development environments, verify their output or keep correcting it. Agents fill these gaps by wrapping the model in loops of planning, acting, observing and refining. The authors argue that agents differ from earlier code generation in three ways: they work autonomously, they cover most of the software development life cycle, and research attention has moved from model algorithms to engineering concerns such as workflow management, reliability and tool integration. Using a systematic literature search, they organize the field by technique rather than by application scenario, which is how prior English-language surveys grouped it. They also map applications across the development cycle, catalogue benchmarks and metrics, and compare commercial tools. The result is a method-centred reference that ends with a five-part research agenda on open problems.

## Key points
- **Corpus:** The search covered January 2022 to June 2025 across ACM DL, IEEE Xplore, SpringerLink, Google Scholar, DBLP and CNKI, limited to CCF A/B venues plus high-impact arXiv preprints. It produced 447 candidates, filtered to 100 core papers. arXiv supplied 44 of them, ACL 12, ICML 11 and ICLR 9. No relevant papers appeared in 2022.
- **Taxonomy:** Single-agent techniques fall into three groups:
  - planning and reasoning (e.g., Self-planning, CodeAct, CodePlan);
  - tool integration and retrieval augmentation;
  - reflection and self-improvement.

  Multi-agent systems are treated separately. The survey names Self-collaboration (April 2023) as the earliest LLM multi-agent software development framework, followed by work such as ChatDev, MetaGPT and SWE-agent.
- **Tools compared:** GitHub Copilot, Devin, Cursor, Tongyi Lingma and Claude Code (200k-token context). They are grouped into three roles: programming assistant, collaborative partner and autonomous development team. Devin is singled out for low real-world reliability.
- **Challenges:** The authors list five areas:
  - core capability limits, including context engineering failures (poisoned, distracting, confusing or conflicting context);
  - error cascades in multi-agent pipelines and continual learning;
  - tool safety, high running cost and edge deployment;
  - hallucination, malicious code and copyright;
  - evaluation that relies too heavily on pass@k.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{2025,
  title = {{基于大语言模型智能体的代码生成综述}},
  author = {董益宏 and 姜雪 and 钱家如 and 王天 and 张克驰 and 金芝 and 李戈},
  journal = {软件学报 (Journal of Software)},
  year = {2025},
  doi = {10.13328/j.cnki.jos.007593},
  url = {https://doi.org/10.13328/j.cnki.jos.007593}
}
```

- [← previous 基于大模型的具身智能系统综述](https://md.research.elamur.ai/free/papers/wang-2025-embodied-intelligence-systems/)
- [next → 基于大语言模型的中文实体链接实证研究](https://md.research.elamur.ai/free/papers/xu-2025-empirical-study-chinese/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/papers/2025/page/3/)

## References

169 references, 28 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Kitzelmann E. Inductive programming: A survey of program synthesis techniques. In: Proc. of the 3rd Int’l Workshop on Approaches and Applications of Inductive Programming (AAIP). Edinburgh: Springer, 2010. 50–73. [doi: 10.1007/978-3-642-11931-6_3] — [doi:10.1007/978-3-642-11931-6_3](https://doi.org/10.1007/978-3-642-11931-6_3) [find](https://scholar.google.com/scholar?q=Kitzelmann+E%2E+Inductive+programming%3A+A+survey+of+program+synthesis+techniques%2E+In%3A+Proc%2E+of+the+3rd+Int%E2%80%99l+Workshop+on+Approaches+and+Applications+of+Inductive+Programming+%28AAIP%29%2E+Edinburgh%3A+Springer%2C+)
2. Ling W, Grefenstette E, Hermann KM, Kočiský T, Senior A, Wang FM, Blunsom P. Latent predictor networks for code generation. In: Proc. of the 54th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Berlin: ACL, 2016. 599–609. [doi: 10.18653/v1/P16-1057] [find](https://scholar.google.com/scholar?q=Ling+W%2C+Grefenstette+E%2C+Hermann+KM%2C+Ko%C4%8Disk%C3%BD+T%2C+Senior+A%2C+Wang+FM%2C+Blunsom+P%2E+Latent+predictor+networks+for+code+generation%2E+In%3A+Proc%2E+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Li)
3. Yin PC, Neubig G. A syntactic neural model for general-purpose code generation. In: Proc. of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Vancouver: ACL, 2017. 440–450. [doi: 10.18653/v1/P17-1041] [find](https://scholar.google.com/scholar?q=Yin+PC%2C+Neubig+G%2E+A+syntactic+neural+model+for+general%2Dpurpose+code+generation%2E+In%3A+Proc%2E+of+the+55th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Vol%2E+1%3A+Long+Papers%29%2E+Vancouver%3A+) [our card](https://md.research.elamur.ai/free/papers/yin-2017-syntactic-neural-model-general-purpose/)
4. Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E, Lample G. LLaMA: Open and efficient foundation language models. arXiv:2302.13971, 2023. [find](https://scholar.google.com/scholar?q=Touvron+H%2C+Lavril+T%2C+Izacard+G%2C+Martinet+X%2C+Lachaux+MA%2C+Lacroix+T%2C+Rozi%C3%A8re+B%2C+Goyal+N%2C+Hambro+E%2C+Azhar+F%2C+Rodriguez+A%2C+Joulin+A%2C+Grave+E%2C+Lample+G%2E+LLaMA%3A+Open+and+efficient+foundation+language+models)
5. Touvron H, Martin L, Stone K, et al. LLaMA 2: Open foundation and fine-tuned chat models. arXiv:2307.09288, 2023. [find](https://scholar.google.com/scholar?q=Touvron+H%2C+Martin+L%2C+Stone+K%2C+et+al%2E+LLaMA+2%3A+Open+foundation+and+fine%2Dtuned+chat+models%2E+arXiv%3A2307%2E09288%2C+2023%2E)
6. Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J, Hilton J, Kelton F, Miller L, Simens M, Askell A, Welinder P, Christiano P, Leike J, Lowe R. Training language models to follow instructions with human feedback. In: Proc. of the 36th Int’l… [find](https://scholar.google.com/scholar?q=Ouyang+L%2C+Wu+J%2C+Jiang+X%2C+Almeida+D%2C+Wainwright+CL%2C+Mishkin+P%2C+Zhang+C%2C+Agarwal+S%2C+Slama+K%2C+Ray+A%2C+Schulman+J%2C+Hilton+J%2C+Kelton+F%2C+Miller+L%2C+Simens+M%2C+Askell+A%2C+Welinder+P%2C+Christiano+P%2C+Leike+J%2C+Lowe+)
7. Rozière B, Gehring J, Gloeckle F, et al. Code Llama: Open foundation models for code. arXiv:2308.12950, 2024. [find](https://scholar.google.com/scholar?q=Rozi%C3%A8re+B%2C+Gehring+J%2C+Gloeckle+F%2C+et+al%2E+Code+Llama%3A+Open+foundation+models+for+code%2E+arXiv%3A2308%2E12950%2C+2024%2E) [our card](https://md.research.elamur.ai/free/papers/roziere-2024-code-llama-open-foundation-models/)
8. Wang Y, Wang WS, Joty S, Hoi SCH. CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In: Proc. of the 2021 Conf. on Empirical Methods in Natural Language Processing (EMNLP). Punta Cana: ACL, 2021. 8696–8708. [doi: 10.18653/v1/2021.emnlp-main.68… [find](https://scholar.google.com/scholar?q=Wang+Y%2C+Wang+WS%2C+Joty+S%2C+Hoi+SCH%2E+CodeT5%3A+Identifier%2Daware+unified+pre%2Dtrained+encoder%2Ddecoder+models+for+code+understanding+and+generation%2E+In%3A+Proc%2E+of+the+2021+Conf%2E+on+Empirical+Methods+in+Natural)
9. Li YJ, Choi D, Chung J, et al. Competition-level code generation with AlphaCode. Science, 2022, 378(6624): 1092–1097. [doi: 10.1126/science.abq1158] [find](https://scholar.google.com/scholar?q=Li+YJ%2C+Choi+D%2C+Chung+J%2C+et+al%2E+Competition%2Dlevel+code+generation+with+AlphaCode%2E+Science%2C+2022%2C+378%286624%29%3A+1092%E2%80%931097%2E+%5Bdoi%3A+10%2E1126%2Fscience%2Eabq1158%5D) [our card](https://md.research.elamur.ai/free/papers/li-2022-competition-level-code-generation-alphacode/)
10. Jiang X, Dong YH, Wang LC, Fang Z, Shang QW, Li G, Jin Z, Jiao WP. Self-planning code generation with large language models. ACM Trans. on Software Engineering and Methodology, 2024, 33(7): 182. [doi: 10.1145/3672456] [find](https://scholar.google.com/scholar?q=Jiang+X%2C+Dong+YH%2C+Wang+LC%2C+Fang+Z%2C+Shang+QW%2C+Li+G%2C+Jin+Z%2C+Jiao+WP%2E+Self%2Dplanning+code+generation+with+large+language+models%2E+ACM+Trans%2E+on+Software+Engineering+and+Methodology%2C+2024%2C+33%287%29%3A+182%2E+%5Bdoi%3A)
11. Le H, Chen HL, Saha A, Gokul A, Sahoo D, Joty S. CodeChain: Towards modular code generation through chain of self-revisions with representative sub-modules. In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.net, 2024. 1–31. [find](https://scholar.google.com/scholar?q=Le+H%2C+Chen+HL%2C+Saha+A%2C+Gokul+A%2C+Sahoo+D%2C+Joty+S%2E+CodeChain%3A+Towards+modular+code+generation+through+chain+of+self%2Drevisions+with+representative+sub%2Dmodules%2E+In%3A+Proc%2E+of+the+12th+Int%E2%80%99l+Conf%2E+on+Learni)
12. Pan RW, Zhang HY, Liu C. CodeCoR: An LLM-based self-reflective multi-agent framework for code generation. arXiv:2501.07811, 2025. — [arXiv:2501.07811](https://arxiv.org/abs/2501.07811) [find](https://scholar.google.com/scholar?q=Pan+RW%2C+Zhang+HY%2C+Liu+C%2E+CodeCoR%3A+An+LLM%2Dbased+self%2Dreflective+multi%2Dagent+framework+for+code+generation%2E+arXiv%3A2501%2E07811%2C+2025%2E+%E2%80%94+%5BarXiv%3A2501%2E07811%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2501%2E07811%29)
13. Rasheed Z, Waseem M, Saari M, Systä K, Abrahamsson P. CodePori: Large scale model for autonomous software development by using multi-agents. arXiv:2402.01411, 2024. — [arXiv:2402.01411](https://arxiv.org/abs/2402.01411) [find](https://scholar.google.com/scholar?q=Rasheed+Z%2C+Waseem+M%2C+Saari+M%2C+Syst%C3%A4+K%2C+Abrahamsson+P%2E+CodePori%3A+Large+scale+model+for+autonomous+software+development+by+using+multi%2Dagents%2E+arXiv%3A2402%2E01411%2C+2024%2E+%E2%80%94+%5BarXiv%3A2402%2E01411%5D%28https%3A%2F%2Farxiv%2E)
14. Manish S. An autonomous multi-agent LLM framework for agile software development. Int’l Journal of Trend in Scientific Research and Development, 2024, 8(5): 892–898. [doi: 10.5281/zenodo.14179129] — [doi:10.5281/zenodo.14179129](https://doi.org/10.5281/zenodo.14179129) [find](https://scholar.google.com/scholar?q=Manish+S%2E+An+autonomous+multi%2Dagent+LLM+framework+for+agile+software+development%2E+Int%E2%80%99l+Journal+of+Trend+in+Scientific+Research+and+Development%2C+2024%2C+8%285%29%3A+892%E2%80%93898%2E+%5Bdoi%3A+10%2E5281%2Fzenodo%2E14179129%5D+%E2%80%94+%5B)
15. Dong YH, Jiang X, Jin Z, Li G. Self-collaboration code generation via ChatGPT. ACM Trans. on Software Engineering and Methodology, 2024, 33(7): 189. [doi: 10.1145/3672459] [find](https://scholar.google.com/scholar?q=Dong+YH%2C+Jiang+X%2C+Jin+Z%2C+Li+G%2E+Self%2Dcollaboration+code+generation+via+ChatGPT%2E+ACM+Trans%2E+on+Software+Engineering+and+Methodology%2C+2024%2C+33%287%29%3A+189%2E+%5Bdoi%3A+10%2E1145%2F3672459%5D)
16. Qian C, Liu W, Liu HZ, Chen N, Dang YF, Li JH, Yang C, Chen WZ, Su YS, Cong X, Xu JY, Li DH, Liu ZY, Sun MS. ChatDev: Communicative agents for software development. In: Proc. of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Bangkok: ACL, 2024. 15174–… [find](https://scholar.google.com/scholar?q=Qian+C%2C+Liu+W%2C+Liu+HZ%2C+Chen+N%2C+Dang+YF%2C+Li+JH%2C+Yang+C%2C+Chen+WZ%2C+Su+YS%2C+Cong+X%2C+Xu+JY%2C+Li+DH%2C+Liu+ZY%2C+Sun+MS%2E+ChatDev%3A+Communicative+agents+for+software+development%2E+In%3A+Proc%2E+of+the+62nd+Annual+Meetin) [our card](https://md.research.elamur.ai/free/papers/qian-2023-chatdev-communicative-agents-software/)
17. Hong SR, Zhuge MC, Chen J, Zheng XW, Cheng YH, Wang JL, Zhang CY, Wang ZL, Yau SKS, Lin ZJ, Zhou LY, Ran CY, Xiao LF, Wu CL, Schmidhuber J. MetaGPT: Meta programming for a multi-agent collaborative framework. In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.ne… [find](https://scholar.google.com/scholar?q=Hong+SR%2C+Zhuge+MC%2C+Chen+J%2C+Zheng+XW%2C+Cheng+YH%2C+Wang+JL%2C+Zhang+CY%2C+Wang+ZL%2C+Yau+SKS%2C+Lin+ZJ%2C+Zhou+LY%2C+Ran+CY%2C+Xiao+LF%2C+Wu+CL%2C+Schmidhuber+J%2E+MetaGPT%3A+Meta+programming+for+a+multi%2Dagent+collaborative+fr) [our card](https://md.research.elamur.ai/free/papers/hong-2023-metagpt-meta-programming-multi/)
18. Lu S, Guo DY, Ren S, Huang JJ, Svyatkovskiy A, Blanco A, Clement CB, Drain D, Jiang DX, Tang DY, Li G, Zhou LD, Shou LJ, Zhou L, Tufano M, Gong M, Zhou M, Duan N, Sundaresan N, Deng SK, Fu SY, Liu SJ. CodeXGLUE: A machine learning benchmark dataset for code understanding and generation. In: Proc. of… [find](https://scholar.google.com/scholar?q=Lu+S%2C+Guo+DY%2C+Ren+S%2C+Huang+JJ%2C+Svyatkovskiy+A%2C+Blanco+A%2C+Clement+CB%2C+Drain+D%2C+Jiang+DX%2C+Tang+DY%2C+Li+G%2C+Zhou+LD%2C+Shou+LJ%2C+Zhou+L%2C+Tufano+M%2C+Gong+M%2C+Zhou+M%2C+Duan+N%2C+Sundaresan+N%2C+Deng+SK%2C+Fu+SY%2C+Liu+SJ%2E)
19. Mu FW, Shi L, Wang S, Yu ZH, Zhang BQ, Wang CX, Liu SC, Wang Q. ClarifyGPT: Empowering LLM-based code generation with intention clarification. arXiv:2310.10996, 2023. — [arXiv:2310.10996](https://arxiv.org/abs/2310.10996) [find](https://scholar.google.com/scholar?q=Mu+FW%2C+Shi+L%2C+Wang+S%2C+Yu+ZH%2C+Zhang+BQ%2C+Wang+CX%2C+Liu+SC%2C+Wang+Q%2E+ClarifyGPT%3A+Empowering+LLM%2Dbased+code+generation+with+intention+clarification%2E+arXiv%3A2310%2E10996%2C+2023%2E+%E2%80%94+%5BarXiv%3A2310%2E10996%5D%28https%3A%2F%2Farxi)
20. Wang ZT, Wang W, Li ZR, Wang L, Yi C, Xu XJ, Cao LY, Su HJ, Chen SZ, Zhou J. XUAT-Copilot: Multi-agent collaborative system for automated user acceptance testing with large language model. arXiv:2401.02705, 2024. [find](https://scholar.google.com/scholar?q=Wang+ZT%2C+Wang+W%2C+Li+ZR%2C+Wang+L%2C+Yi+C%2C+Xu+XJ%2C+Cao+LY%2C+Su+HJ%2C+Chen+SZ%2C+Zhou+J%2E+XUAT%2DCopilot%3A+Multi%2Dagent+collaborative+system+for+automated+user+acceptance+testing+with+large+language+model%2E+arXiv%3A2401%2E)
21. Zhang K, Zhang CX, Wang C, Zhang C, Wu YC, Xing ZC, Liu Y, Li QS, Peng X. LogiAgent: Automated logical testing for REST systems with LLM-based multi-agents. arXiv:2503.15079, 2025. — [arXiv:2503.15079](https://arxiv.org/abs/2503.15079) [find](https://scholar.google.com/scholar?q=Zhang+K%2C+Zhang+CX%2C+Wang+C%2C+Zhang+C%2C+Wu+YC%2C+Xing+ZC%2C+Liu+Y%2C+Li+QS%2C+Peng+X%2E+LogiAgent%3A+Automated+logical+testing+for+REST+systems+with+LLM%2Dbased+multi%2Dagents%2E+arXiv%3A2503%2E15079%2C+2025%2E+%E2%80%94+%5BarXiv%3A2503%2E15079)
22. Baumgartner N, Iyenghar P, Schoemaker T, Pulvermüller E. AI-driven refactoring: A pipeline for identifying and correcting data clumps in Git repositories. Electronics, 2024, 13(9): 1644. [doi: 10.3390/electronics13091644] — [doi:10.3390/electronics13091644](https://doi.org/10.3390/electronics13091644) [find](https://scholar.google.com/scholar?q=Baumgartner+N%2C+Iyenghar+P%2C+Schoemaker+T%2C+Pulverm%C3%BCller+E%2E+AI%2Ddriven+refactoring%3A+A+pipeline+for+identifying+and+correcting+data+clumps+in+Git+repositories%2E+Electronics%2C+2024%2C+13%289%29%3A+1644%2E+%5Bdoi%3A+10%2E3390)
23. Rabinovich M, Stern M, Klein D. Abstract syntax networks for code generation and semantic parsing. In: Proc. of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Vancouver: ACL, 2017. 1139–1149. [doi: 10.18653/v1/P17-1105] [find](https://scholar.google.com/scholar?q=Rabinovich+M%2C+Stern+M%2C+Klein+D%2E+Abstract+syntax+networks+for+code+generation+and+semantic+parsing%2E+In%3A+Proc%2E+of+the+55th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Vol%2E+1%3A+Long+P)
24. Guo DY, Ren S, Lu S, Feng ZY, Tang DY, Liu SJ, Zhou L, Duan N, Svyatkovskiy A, Fu SY, Tufano M, Deng SK, Clement C, Drain D, Sundaresan N, Yin J, Jiang DX, Zhou M. GraphCodeBERT: Pre-training code representations with data flow. In: Proc. of the 9th Int’l Conf. on Learning Representations (ICLR). Op… [find](https://scholar.google.com/scholar?q=Guo+DY%2C+Ren+S%2C+Lu+S%2C+Feng+ZY%2C+Tang+DY%2C+Liu+SJ%2C+Zhou+L%2C+Duan+N%2C+Svyatkovskiy+A%2C+Fu+SY%2C+Tufano+M%2C+Deng+SK%2C+Clement+C%2C+Drain+D%2C+Sundaresan+N%2C+Yin+J%2C+Jiang+DX%2C+Zhou+M%2E+GraphCodeBERT%3A+Pre%2Dtraining+code+rep) [our card](https://md.research.elamur.ai/free/papers/guo-2021-graphcodebert-pre-training-code-representations/)
25. Guo DY, Lu S, Duan N, Wang YL, Zhou M, Yin J. UniXcoder: Unified cross-modal pre-training for code representation. In: Proc. of the 60th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Dublin: ACL, 2022. 7212–7225. [doi: 10.18653/v1/2022.acl-long.499] [find](https://scholar.google.com/scholar?q=Guo+DY%2C+Lu+S%2C+Duan+N%2C+Wang+YL%2C+Zhou+M%2C+Yin+J%2E+UniXcoder%3A+Unified+cross%2Dmodal+pre%2Dtraining+for+code+representation%2E+In%3A+Proc%2E+of+the+60th+Annual+Meeting+of+the+Association+for+Computational+Linguistics) [our card](https://md.research.elamur.ai/free/papers/guo-2022-unixcoder-unified-cross-modal-pre/)
26. Hu YJ, Zhou Q, Chen QH, Li XP, Liu LB, Zhang DJ, Kachroo A, Oz T, Tripp O. QualityFlow: An agentic workflow for program synthesis controlled by LLM quality checks. arXiv:2501.17167, 2025. — [arXiv:2501.17167](https://arxiv.org/abs/2501.17167) [find](https://scholar.google.com/scholar?q=Hu+YJ%2C+Zhou+Q%2C+Chen+QH%2C+Li+XP%2C+Liu+LB%2C+Zhang+DJ%2C+Kachroo+A%2C+Oz+T%2C+Tripp+O%2E+QualityFlow%3A+An+agentic+workflow+for+program+synthesis+controlled+by+LLM+quality+checks%2E+arXiv%3A2501%2E17167%2C+2025%2E+%E2%80%94+%5BarXiv%3A250)
27. Ishibashi Y, Nishimura Y. Self-organized agents: A LLM multi-agent framework toward ultra large-scale code generation and optimization. arXiv:2404.02183, 2024. — [arXiv:2404.02183](https://arxiv.org/abs/2404.02183) [find](https://scholar.google.com/scholar?q=Ishibashi+Y%2C+Nishimura+Y%2E+Self%2Dorganized+agents%3A+A+LLM+multi%2Dagent+framework+toward+ultra+large%2Dscale+code+generation+and+optimization%2E+arXiv%3A2404%2E02183%2C+2024%2E+%E2%80%94+%5BarXiv%3A2404%2E02183%5D%28https%3A%2F%2Farxiv%2Eorg%2Fa)
28. Zhang KC, Li J, Li G, Shi XJ, Jin Z. CodeAgent: Enhancing code generation with tool-integrated agent systems for real-world repo-level coding challenges. In: Proc. of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Bangkok: ACL, 2024. 13643–13658. [doi… [find](https://scholar.google.com/scholar?q=Zhang+KC%2C+Li+J%2C+Li+G%2C+Shi+XJ%2C+Jin+Z%2E+CodeAgent%3A+Enhancing+code+generation+with+tool%2Dintegrated+agent+systems+for+real%2Dworld+repo%2Dlevel+coding+challenges%2E+In%3A+Proc%2E+of+the+62nd+Annual+Meeting+of+the+As)
29. Wang RX, Han XD, Ji L, Wang S, Baldwin T, Li HN. ToolGen: Unified tool retrieval and calling via generation. In: Proc. of the 13th Int’l Conf. on Learning Representations (ICLR). Singapore: OpenReview.net, 2025. 1–26. [find](https://scholar.google.com/scholar?q=Wang+RX%2C+Han+XD%2C+Ji+L%2C+Wang+S%2C+Baldwin+T%2C+Li+HN%2E+ToolGen%3A+Unified+tool+retrieval+and+calling+via+generation%2E+In%3A+Proc%2E+of+the+13th+Int%E2%80%99l+Conf%2E+on+Learning+Representations+%28ICLR%29%2E+Singapore%3A+OpenReview)
30. Sapkota R, Roumeliotis KI, Karkee M. Vibe coding vs. agentic coding: Fundamentals and practical implications of agentic AI. arXiv:2505.19443, 2025. — [arXiv:2505.19443](https://arxiv.org/abs/2505.19443) [find](https://scholar.google.com/scholar?q=Sapkota+R%2C+Roumeliotis+KI%2C+Karkee+M%2E+Vibe+coding+vs%2E+agentic+coding%3A+Fundamentals+and+practical+implications+of+agentic+AI%2E+arXiv%3A2505%2E19443%2C+2025%2E+%E2%80%94+%5BarXiv%3A2505%2E19443%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2505%2E1944)
31. Schäfer M, Nadi S, Eghbali A, Tip F. Adaptive test generation using a large language model. arXiv:2302.06527, 2023. — [arXiv:2302.06527](https://arxiv.org/abs/2302.06527) [find](https://scholar.google.com/scholar?q=Sch%C3%A4fer+M%2C+Nadi+S%2C+Eghbali+A%2C+Tip+F%2E+Adaptive+test+generation+using+a+large+language+model%2E+arXiv%3A2302%2E06527%2C+2023%2E+%E2%80%94+%5BarXiv%3A2302%2E06527%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2302%2E06527%29)
32. Xu QH, Wang GC, Briand L, Liu K. A multi-agent LLM-based JUit test generation with strong oracles. arXiv:2506.02943, 2025. — [arXiv:2506.02943](https://arxiv.org/abs/2506.02943) [find](https://scholar.google.com/scholar?q=Xu+QH%2C+Wang+GC%2C+Briand+L%2C+Liu+K%2E+A+multi%2Dagent+LLM%2Dbased+JUit+test+generation+with+strong+oracles%2E+arXiv%3A2506%2E02943%2C+2025%2E+%E2%80%94+%5BarXiv%3A2506%2E02943%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2506%2E02943%29)
33. Dearing MT, Tao YH, Wu XF, Lan ZL, Taylor V. Leveraging LLMs to automate energy-aware refactoring of parallel scientific codes. arXiv:2505.02184, 2025. [find](https://scholar.google.com/scholar?q=Dearing+MT%2C+Tao+YH%2C+Wu+XF%2C+Lan+ZL%2C+Taylor+V%2E+Leveraging+LLMs+to+automate+energy%2Daware+refactoring+of+parallel+scientific+codes%2E+arXiv%3A2505%2E02184%2C+2025%2E)
34. Peng HY, Gupte A, Hasler R, Eliopoulos NJ, Ho CC, Mantri R, Deng L, Läufer K, Thiruvathukal GK, Davis JC. SysLLMatic: Large language models are software system optimizers. arXiv:2506.01249, 2025. — [arXiv:2506.01249](https://arxiv.org/abs/2506.01249) [find](https://scholar.google.com/scholar?q=Peng+HY%2C+Gupte+A%2C+Hasler+R%2C+Eliopoulos+NJ%2C+Ho+CC%2C+Mantri+R%2C+Deng+L%2C+L%C3%A4ufer+K%2C+Thiruvathukal+GK%2C+Davis+JC%2E+SysLLMatic%3A+Large+language+models+are+software+system+optimizers%2E+arXiv%3A2506%2E01249%2C+2025%2E+%E2%80%94+%5Ba)
35. Shi WX, Zhang YH, Xing XY, Xu J. Harnessing large language models for seed generation in greybox fuzzing. arXiv:2411.18143, 2024. — [arXiv:2411.18143](https://arxiv.org/abs/2411.18143) [find](https://scholar.google.com/scholar?q=Shi+WX%2C+Zhang+YH%2C+Xing+XY%2C+Xu+J%2E+Harnessing+large+language+models+for+seed+generation+in+greybox+fuzzing%2E+arXiv%3A2411%2E18143%2C+2024%2E+%E2%80%94+%5BarXiv%3A2411%2E18143%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2411%2E18143%29)
36. Foster C, Gulati A, Harman M, Harper I, Mao K, Ritchey J, Robert H, Sengupta S. Mutation-guided LLM-based test generation at meta. In: Proc. of the 33rd ACM Int’l Conf. on the Foundations of Software Engineering. Trondheim: ACM, 2025. 180–191. [doi: 10.1145/3696630.3728544] — [doi:10.1145/3696630.3728544](https://doi.org/10.1145/3696630.3728544) [find](https://scholar.google.com/scholar?q=Foster+C%2C+Gulati+A%2C+Harman+M%2C+Harper+I%2C+Mao+K%2C+Ritchey+J%2C+Robert+H%2C+Sengupta+S%2E+Mutation%2Dguided+LLM%2Dbased+test+generation+at+meta%2E+In%3A+Proc%2E+of+the+33rd+ACM+Int%E2%80%99l+Conf%2E+on+the+Foundations+of+Software+)
37. Jin HL, Huang LH, Cai HP, Yan J, Li B, Chen HM. From LLMs to LLM-based agents for software engineering: A survey of current, challenges and future. arXiv:2408.02479, 2024. — [arXiv:2408.02479](https://arxiv.org/abs/2408.02479) [find](https://scholar.google.com/scholar?q=Jin+HL%2C+Huang+LH%2C+Cai+HP%2C+Yan+J%2C+Li+B%2C+Chen+HM%2E+From+LLMs+to+LLM%2Dbased+agents+for+software+engineering%3A+A+survey+of+current%2C+challenges+and+future%2E+arXiv%3A2408%2E02479%2C+2024%2E+%E2%80%94+%5BarXiv%3A2408%2E02479%5D%28https%3A%2F)
38. Liu JW, Wang KX, Chen YX, Peng X, Chen ZP, Zhang LM, Lou YL. Large language model-based agents for software engineering: A survey. arXiv:2409.02977, 2024. — [arXiv:2409.02977](https://arxiv.org/abs/2409.02977) [find](https://scholar.google.com/scholar?q=Liu+JW%2C+Wang+KX%2C+Chen+YX%2C+Peng+X%2C+Chen+ZP%2C+Zhang+LM%2C+Lou+YL%2E+Large+language+model%2Dbased+agents+for+software+engineering%3A+A+survey%2E+arXiv%3A2409%2E02977%2C+2024%2E+%E2%80%94+%5BarXiv%3A2409%2E02977%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F24)
39. He JD, Treude C, Lo D. LLM-based multi-agent systems for software engineering: Literature review, vision, and the road ahead. ACM Trans. on Software Engineering and Methodology, 2025, 34(5): 124. [doi: 10.1145/3712003] — [doi:10.1145/3712003](https://doi.org/10.1145/3712003) [find](https://scholar.google.com/scholar?q=He+JD%2C+Treude+C%2C+Lo+D%2E+LLM%2Dbased+multi%2Dagent+systems+for+software+engineering%3A+Literature+review%2C+vision%2C+and+the+road+ahead%2E+ACM+Trans%2E+on+Software+Engineering+and+Methodology%2C+2025%2C+34%285%29%3A+124%2E+%5Bdoi)
40. Wang YL, Zhong WJ, Huang YX, Shi ES, Yang M, Chen JC, Li H, Ma YC, Wang QX, Zheng ZB. Agents in software engineering: Survey, landscape, and vision. Automated Software Engineering, 2025, 32(2): 70. [doi: 10.1007/s10515-025-00544-2] — [doi:10.1007/s10515-025-00544-2](https://doi.org/10.1007/s10515-025-00544-2) [find](https://scholar.google.com/scholar?q=Wang+YL%2C+Zhong+WJ%2C+Huang+YX%2C+Shi+ES%2C+Yang+M%2C+Chen+JC%2C+Li+H%2C+Ma+YC%2C+Wang+QX%2C+Zheng+ZB%2E+Agents+in+software+engineering%3A+Survey%2C+landscape%2C+and+vision%2E+Automated+Software+Engineering%2C+2025%2C+32%282%29%3A+70%2E+%5Bd)
41. Svyatkovskiy A, Deng SK, Fu SY, Sundaresan N. Intellicode compose: Code generation using Transformer. In: Proc. of the 28th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering (ESEC/FSE). ACM, 2020. 1433–1443. [doi: 10.1145/3368089.3417058] [find](https://scholar.google.com/scholar?q=Svyatkovskiy+A%2C+Deng+SK%2C+Fu+SY%2C+Sundaresan+N%2E+Intellicode+compose%3A+Code+generation+using+Transformer%2E+In%3A+Proc%2E+of+the+28th+ACM+Joint+Meeting+on+European+Software+Engineering+Conf%2E+and+Symp%2E+on+the+Fo)
42. Herrington J. Code Generation in Action. Greenwich: Manning Publications Co., 2003. [find](https://scholar.google.com/scholar?q=Herrington+J%2E+Code+Generation+in+Action%2E+Greenwich%3A+Manning+Publications+Co%2E%2C+2003%2E)
43. Becker BA, Denny P, Finnie-Ansley J, Luxton-Reilly A, Prather J, Santos EA. Programming is hard-or at least it used to be: Educational opportunities and challenges of AI code generation. In: Proc. of the 54th ACM Technical Symp. on Computer Science Education V.1 (SIGCSE). Toronto: ACM, 2023. 500–506… [find](https://scholar.google.com/scholar?q=Becker+BA%2C+Denny+P%2C+Finnie%2DAnsley+J%2C+Luxton%2DReilly+A%2C+Prather+J%2C+Santos+EA%2E+Programming+is+hard%2Dor+at+least+it+used+to+be%3A+Educational+opportunities+and+challenges+of+AI+code+generation%2E+In%3A+Proc%2E+of+)
44. Xu FF, Vasilescu B, Neubig G. In-IDE code generation from natural language: Promise and challenges. ACM Trans. on Software Engineering and Methodology, 2022, 31(2): 29. [doi: 10.1145/3487569] — [doi:10.1145/3487569](https://doi.org/10.1145/3487569) [find](https://scholar.google.com/scholar?q=Xu+FF%2C+Vasilescu+B%2C+Neubig+G%2E+In%2DIDE+code+generation+from+natural+language%3A+Promise+and+challenges%2E+ACM+Trans%2E+on+Software+Engineering+and+Methodology%2C+2022%2C+31%282%29%3A+29%2E+%5Bdoi%3A+10%2E1145%2F3487569%5D+%E2%80%94+%5Bdoi%3A1)
45. Huynh N, Lin BY. Large language models for code generation: A comprehensive survey of challenges, techniques, evaluation, and applications. arXiv:2503.01245, 2025. — [arXiv:2503.01245](https://arxiv.org/abs/2503.01245) [find](https://scholar.google.com/scholar?q=Huynh+N%2C+Lin+BY%2E+Large+language+models+for+code+generation%3A+A+comprehensive+survey+of+challenges%2C+techniques%2C+evaluation%2C+and+applications%2E+arXiv%3A2503%2E01245%2C+2025%2E+%E2%80%94+%5BarXiv%3A2503%2E01245%5D%28https%3A%2F%2Farxiv%2Eo)
46. Kasneci E, Sessler K, Küchemann S, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 2023, 103: 102274. [doi: 10.1016/j.lindif.2023.102274] [find](https://scholar.google.com/scholar?q=Kasneci+E%2C+Sessler+K%2C+K%C3%BCchemann+S%2C+et+al%2E+ChatGPT+for+good%3F+On+opportunities+and+challenges+of+large+language+models+for+education%2E+Learning+and+Individual+Differences%2C+2023%2C+103%3A+102274%2E+%5Bdoi%3A+10%2E101) [our card](https://md.research.elamur.ai/free/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
47. Chang YP, Wang X, Wang JD, Wu Y, Yang LY, Zhu KJ, Chen H, Yi XY, Wang CX, Wang YD, Ye W, Zhang Y, Chang Y, Yu PS, Yang Q, Xie X. A survey on evaluation of large language models. ACM Trans. on Intelligent Systems and Technology, 2024, 15(3): 39. [doi: 10.1145/3641289] [find](https://scholar.google.com/scholar?q=Chang+YP%2C+Wang+X%2C+Wang+JD%2C+Wu+Y%2C+Yang+LY%2C+Zhu+KJ%2C+Chen+H%2C+Yi+XY%2C+Wang+CX%2C+Wang+YD%2C+Ye+W%2C+Zhang+Y%2C+Chang+Y%2C+Yu+PS%2C+Yang+Q%2C+Xie+X%2E+A+survey+on+evaluation+of+large+language+models%2E+ACM+Trans%2E+on+Intellig) [our card](https://md.research.elamur.ai/free/papers/chang-2023-survey-evaluation-large-language-models/)
48. Zhao WX, Zhou K, Li JY, Tang T, Wang X, Hou Y, Min Y, Zhang B, Zhang J, Dong Z, Du Y, Yang C, Chen Y, Chen Z, Jiang J, Ren R, Li Y, Tang X, Liu Z, Liu P, Nie JY, Wen JR. A survey of large language models. arXiv:2303.18223, 2023. [find](https://scholar.google.com/scholar?q=Zhao+WX%2C+Zhou+K%2C+Li+JY%2C+Tang+T%2C+Wang+X%2C+Hou+Y%2C+Min+Y%2C+Zhang+B%2C+Zhang+J%2C+Dong+Z%2C+Du+Y%2C+Yang+C%2C+Chen+Y%2C+Chen+Z%2C+Jiang+J%2C+Ren+R%2C+Li+Y%2C+Tang+X%2C+Liu+Z%2C+Liu+P%2C+Nie+JY%2C+Wen+JR%2E+A+survey+of+large+language+mod) [our card](https://md.research.elamur.ai/free/papers/zhao-2023-survey-large-language-models/)
49. Naveed H, Khan AU, Qiu S, Saqib M, Anwar S, Usman M, Akhtar N, Barnes N, Mian A. A comprehensive overview of large language models. ACM Trans. on Intelligent Systems and Technology, 2025, 16(5): 106. [doi: 10.1145/3744746] [find](https://scholar.google.com/scholar?q=Naveed+H%2C+Khan+AU%2C+Qiu+S%2C+Saqib+M%2C+Anwar+S%2C+Usman+M%2C+Akhtar+N%2C+Barnes+N%2C+Mian+A%2E+A+comprehensive+overview+of+large+language+models%2E+ACM+Trans%2E+on+Intelligent+Systems+and+Technology%2C+2025%2C+16%285%29%3A+106%2E+) [our card](https://md.research.elamur.ai/free/papers/naveed-2023-comprehensive-overview-large-language-models/)
50. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the 31st Int’l Conf. on Neural Information Processing Systems (NeurIPS). Long Beach: Curran Associates Inc., 2017. 6000–6010. [find](https://scholar.google.com/scholar?q=Vaswani+A%2C+Shazeer+N%2C+Parmar+N%2C+Uszkoreit+J%2C+Jones+L%2C+Gomez+AN%2C+Kaiser+%C5%81%2C+Polosukhin+I%2E+Attention+is+all+you+need%2E+In%3A+Proc%2E+of+the+31st+Int%E2%80%99l+Conf%2E+on+Neural+Information+Processing+Systems+%28NeurIPS%29%2E)
51. Chen M, Tworek J, Jun H, et al. Evaluating large language models trained on code. arXiv:2107.03374, 2021. [find](https://scholar.google.com/scholar?q=Chen+M%2C+Tworek+J%2C+Jun+H%2C+et+al%2E+Evaluating+large+language+models+trained+on+code%2E+arXiv%3A2107%2E03374%2C+2021%2E) [our card](https://md.research.elamur.ai/free/papers/chen-2021-evaluating-large-language-models-trained/)
52. Guo DY, Zhu QH, Yang DJ, Xie ZD, Dong K, Zhang WT, Chen GT, Bi X, Wu Y, Li YK, Luo FL, Xiong YF, Liang WF. DeepSeek-Coder: When the large language model meets programming—The rise of code intelligence. arXiv:2401.14196, 2024. [find](https://scholar.google.com/scholar?q=Guo+DY%2C+Zhu+QH%2C+Yang+DJ%2C+Xie+ZD%2C+Dong+K%2C+Zhang+WT%2C+Chen+GT%2C+Bi+X%2C+Wu+Y%2C+Li+YK%2C+Luo+FL%2C+Xiong+YF%2C+Liang+WF%2E+DeepSeek%2DCoder%3A+When+the+large+language+model+meets+programming%E2%80%94The+rise+of+code+intelligence)
53. Hui BY, Yang J, Cui ZY, Yang JX, Liu DH, Zhang L, Liu TY, Zhang JJ, Yu BW, Lu KM, Dang K, Fan Y, Zhang YC, Yang A, Men R, Huang F, Zheng B, Miao YB, Quan SHR, Feng YL, Ren XZ, Ren XC, Zhou JR, Lin JY. Qwen2.5-Coder technical report. arXiv:2409.12186, 2024. [find](https://scholar.google.com/scholar?q=Hui+BY%2C+Yang+J%2C+Cui+ZY%2C+Yang+JX%2C+Liu+DH%2C+Zhang+L%2C+Liu+TY%2C+Zhang+JJ%2C+Yu+BW%2C+Lu+KM%2C+Dang+K%2C+Fan+Y%2C+Zhang+YC%2C+Yang+A%2C+Men+R%2C+Huang+F%2C+Zheng+B%2C+Miao+YB%2C+Quan+SHR%2C+Feng+YL%2C+Ren+XZ%2C+Ren+XC%2C+Zhou+JR%2C+Lin+JY%2E) [our card](https://md.research.elamur.ai/free/papers/hui-2024-qwen2-5-coder-technical-report/)
54. Wei J, Wang XZ, Schuurmans D, Bosma M, Ichter B, Xia F, Chi EH, Le QV, Zhou D. Chain-of-thought prompting elicits reasoning in large language models. In: Proc. of the 36th Int’l Conf. on Neural Information Processing Systems (NeurIPS). New Orleans: Curran Associates Inc., 2022. 1800. [find](https://scholar.google.com/scholar?q=Wei+J%2C+Wang+XZ%2C+Schuurmans+D%2C+Bosma+M%2C+Ichter+B%2C+Xia+F%2C+Chi+EH%2C+Le+QV%2C+Zhou+D%2E+Chain%2Dof%2Dthought+prompting+elicits+reasoning+in+large+language+models%2E+In%3A+Proc%2E+of+the+36th+Int%E2%80%99l+Conf%2E+on+Neural+Inform)
55. Wang EZ, Cassano F, Wu C, Bai YF, Song W, Nath V, Han ZW, Hendryx SM, Yue S, Zhang H. Planning in natural language improves LLM search for code generation. In: Proc. of the 13th Int’l Conf. on Learning Representations (ICLR). Singapore: OpenReview.net, 2025. 1–47. [find](https://scholar.google.com/scholar?q=Wang+EZ%2C+Cassano+F%2C+Wu+C%2C+Bai+YF%2C+Song+W%2C+Nath+V%2C+Han+ZW%2C+Hendryx+SM%2C+Yue+S%2C+Zhang+H%2E+Planning+in+natural+language+improves+LLM+search+for+code+generation%2E+In%3A+Proc%2E+of+the+13th+Int%E2%80%99l+Conf%2E+on+Learnin)
56. Nakano R, Hilton J, Balaji S, Wu J, Ouyang L, Kim C, Hesse C, Jain S, Kosaraju V, Saunders W, Jiang X, Cobbe K, Eloundou T, Krueger G, Button K, Knight M, Chess B, Schulman J. WebGPT: Browser-assisted question-answering with human feedback. arXiv:2112.09332, 2022. [find](https://scholar.google.com/scholar?q=Nakano+R%2C+Hilton+J%2C+Balaji+S%2C+Wu+J%2C+Ouyang+L%2C+Kim+C%2C+Hesse+C%2C+Jain+S%2C+Kosaraju+V%2C+Saunders+W%2C+Jiang+X%2C+Cobbe+K%2C+Eloundou+T%2C+Krueger+G%2C+Button+K%2C+Knight+M%2C+Chess+B%2C+Schulman+J%2E+WebGPT%3A+Browser%2Dassisted) [our card](https://md.research.elamur.ai/free/papers/nakano-2021-webgpt-browser-assisted-question-answering/)
57. Schick T, Dwivedi-Yu J, Dessì R, Raileanu R, Lomeli M, Hambro E, Zettlemoyer L, Cancedda N, Scialom T. Toolformer: Language models can teach themselves to use tools. In: Proc. of the 37th Int’l Conf. on Neural Information Processing Systems (NeurIPS). New Orleans: Curran Associates Inc., 2023. 2997… [find](https://scholar.google.com/scholar?q=Schick+T%2C+Dwivedi%2DYu+J%2C+Dessi%CC%80+R%2C+Raileanu+R%2C+Lomeli+M%2C+Hambro+E%2C+Zettlemoyer+L%2C+Cancedda+N%2C+Scialom+T%2E+Toolformer%3A+Language+models+can+teach+themselves+to+use+tools%2E+In%3A+Proc%2E+of+the+37th+Int%E2%80%99l+Conf%2E) [our card](https://md.research.elamur.ai/free/papers/schick-2023-toolformer-language-models-can-teach/)
58. Gao LY, Madaan A, Zhou SY, Alon U, Liu PF, Yang YM, Callan J, Neubig G. PAL: Program-aided language models. In: Proc. of the 40th Int’l Conf. on Machine Learning (ICML). Honolulu: JMLR.org, 2023. 435. [find](https://scholar.google.com/scholar?q=Gao+LY%2C+Madaan+A%2C+Zhou+SY%2C+Alon+U%2C+Liu+PF%2C+Yang+YM%2C+Callan+J%2C+Neubig+G%2E+PAL%3A+Program%2Daided+language+models%2E+In%3A+Proc%2E+of+the+40th+Int%E2%80%99l+Conf%2E+on+Machine+Learning+%28ICML%29%2E+Honolulu%3A+JMLR%2Eorg%2C+2023%2E+435%2E)
59. Park JS, O’Brien J, Cai CJ, Morris MR, Liang P, Bernstein MS. Generative agents: Interactive simulacra of human behavior. In: Proc. of the 36th Annual ACM Symp. on User Interface Software and Technology (UIST). San Francisco: ACM, 2023. 2. [doi: 10.1145/3586183.3606763] [find](https://scholar.google.com/scholar?q=Park+JS%2C+O%E2%80%99Brien+J%2C+Cai+CJ%2C+Morris+MR%2C+Liang+P%2C+Bernstein+MS%2E+Generative+agents%3A+Interactive+simulacra+of+human+behavior%2E+In%3A+Proc%2E+of+the+36th+Annual+ACM+Symp%2E+on+User+Interface+Software+and+Technolo)
60. Mehta N, Teruel M, Sanz PF, Deng X, Awadallah AH, Kiseleva J. Improving grounded language understanding in a collaborative environment by interacting with agents through help feedback. In: Findings of the Association for Computational Linguistics: EACL 2024. St. Julian’s: ACL, 2024. 1306–1321. [find](https://scholar.google.com/scholar?q=Mehta+N%2C+Teruel+M%2C+Sanz+PF%2C+Deng+X%2C+Awadallah+AH%2C+Kiseleva+J%2E+Improving+grounded+language+understanding+in+a+collaborative+environment+by+interacting+with+agents+through+help+feedback%2E+In%3A+Findings+of)
61. Xi ZH, Chen WX, Guo X, et al. The rise and potential of large language model based agents: A survey. Science China Information Sciences, 2025, 68(2): 121101. [doi: 10.1007/s11432-024-4222-0] [find](https://scholar.google.com/scholar?q=Xi+ZH%2C+Chen+WX%2C+Guo+X%2C+et+al%2E+The+rise+and+potential+of+large+language+model+based+agents%3A+A+survey%2E+Science+China+Information+Sciences%2C+2025%2C+68%282%29%3A+121101%2E+%5Bdoi%3A+10%2E1007%2Fs11432%2D024%2D4222%2D0%5D) [our card](https://md.research.elamur.ai/free/papers/xi-2023-rise-potential-large-language/)
62. Wang L, Ma C, Feng XY, Zhang ZY, Yang H, Zhang JS, Chen ZY, Tang JK, Chen X, Lin YK, Zhao WX, Wei ZW, Wen JR. A survey on large language model based autonomous agents. Frontiers of Computer Science, 2024, 18(6): 186345. [doi: 10.1007/s11704-024-40231-1] [find](https://scholar.google.com/scholar?q=Wang+L%2C+Ma+C%2C+Feng+XY%2C+Zhang+ZY%2C+Yang+H%2C+Zhang+JS%2C+Chen+ZY%2C+Tang+JK%2C+Chen+X%2C+Lin+YK%2C+Zhao+WX%2C+Wei+ZW%2C+Wen+JR%2E+A+survey+on+large+language+model+based+autonomous+agents%2E+Frontiers+of+Computer+Science%2C+2) [our card](https://md.research.elamur.ai/free/papers/wang-2023-survey-large-language-model-based/)
63. Guo TC, Chen XY, Wang YQ, Chang RD, Pei SC, Chawla NV, Wiest O, Zhang XL. Large language model based multi-agents: A survey of progress and challenges. In: Proc. of the 33rd Int’l Joint Conf. on Artificial Intelligence. Jeju: ijcai.org, 2024. 8048–8057. [find](https://scholar.google.com/scholar?q=Guo+TC%2C+Chen+XY%2C+Wang+YQ%2C+Chang+RD%2C+Pei+SC%2C+Chawla+NV%2C+Wiest+O%2C+Zhang+XL%2E+Large+language+model+based+multi%2Dagents%3A+A+survey+of+progress+and+challenges%2E+In%3A+Proc%2E+of+the+33rd+Int%E2%80%99l+Joint+Conf%2E+on+Artif) [our card](https://md.research.elamur.ai/free/papers/guo-2024-large-language-model-based/)
64. Cheng YH, Zhang CY, Zhang ZW, Meng XR, Hong SR, Li WH, Wang ZH, Wang ZK, Yin F, Zhao JH, He XQ. Exploring large language model based intelligent agents: Definitions, methods, and prospects. arXiv:2401.03428, 2024. — [arXiv:2401.03428](https://arxiv.org/abs/2401.03428) [find](https://scholar.google.com/scholar?q=Cheng+YH%2C+Zhang+CY%2C+Zhang+ZW%2C+Meng+XR%2C+Hong+SR%2C+Li+WH%2C+Wang+ZH%2C+Wang+ZK%2C+Yin+F%2C+Zhao+JH%2C+He+XQ%2E+Exploring+large+language+model+based+intelligent+agents%3A+Definitions%2C+methods%2C+and+prospects%2E+arXiv%3A2401)
65. Giray L. Prompt engineering with ChatGPT: A guide for academic writers. Annals of Biomedical Engineering, 2023, 51(12): 2629–2633. [doi: 10.1007/s10439-023-03272-4] — [doi:10.1007/s10439-023-03272-4](https://doi.org/10.1007/s10439-023-03272-4) [find](https://scholar.google.com/scholar?q=Giray+L%2E+Prompt+engineering+with+ChatGPT%3A+A+guide+for+academic+writers%2E+Annals+of+Biomedical+Engineering%2C+2023%2C+51%2812%29%3A+2629%E2%80%932633%2E+%5Bdoi%3A+10%2E1007%2Fs10439%2D023%2D03272%2D4%5D+%E2%80%94+%5Bdoi%3A10%2E1007%2Fs10439%2D023%2D03272%2D4%5D%28)
66. White J, Fu QC, Hays S, Sandborn M, Olea C, Gilbert H, Elnashar A, Spencer-Smith J, Schmidt DC. A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv:2302.11382, 2023. [find](https://scholar.google.com/scholar?q=White+J%2C+Fu+QC%2C+Hays+S%2C+Sandborn+M%2C+Olea+C%2C+Gilbert+H%2C+Elnashar+A%2C+Spencer%2DSmith+J%2C+Schmidt+DC%2E+A+prompt+pattern+catalog+to+enhance+prompt+engineering+with+ChatGPT%2E+arXiv%3A2302%2E11382%2C+2023%2E) [our card](https://md.research.elamur.ai/free/papers/white-2023-prompt-pattern-catalog-enhance-prompt/)
67. Gao YF, Xiong Y, Gao XY, Jia KX, Pan JL, Bi YX, Dai Y, Sun JW, Wang M, Wang HF. Retrieval-augmented generation for large language models: A survey. arXiv:2312.10997, 2023. [find](https://scholar.google.com/scholar?q=Gao+YF%2C+Xiong+Y%2C+Gao+XY%2C+Jia+KX%2C+Pan+JL%2C+Bi+YX%2C+Dai+Y%2C+Sun+JW%2C+Wang+M%2C+Wang+HF%2E+Retrieval%2Daugmented+generation+for+large+language+models%3A+A+survey%2E+arXiv%3A2312%2E10997%2C+2023%2E) [our card](https://md.research.elamur.ai/free/papers/gao-2023-retrieval-augmented-generation-large-language/)
68. Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, Küttler H, Lewis M, Yih WT, Rocktäschel T, Riedel S, Kiela D. Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Proc. of the 34th Int’l Conf. on Neural Information Processing Systems (NeurIPS). Vancouver: Curran Associa… [find](https://scholar.google.com/scholar?q=Lewis+P%2C+Perez+E%2C+Piktus+A%2C+Petroni+F%2C+Karpukhin+V%2C+Goyal+N%2C+K%C3%BCttler+H%2C+Lewis+M%2C+Yih+WT%2C+Rockt%C3%A4schel+T%2C+Riedel+S%2C+Kiela+D%2E+Retrieval%2Daugmented+generation+for+knowledge%2Dintensive+NLP+tasks%2E+In%3A+Proc%2E+o)
69. Li J, Tao CY, Li J, Li G, Jin Z, Zhang HZ, Fang Z, Liu F. Large language model-aware in-context learning for code generation. ACM Trans. on Software Engineering and Methodology, 2025, 34(7): 190. [doi: 10.1145/3715908] — [doi:10.1145/3715908](https://doi.org/10.1145/3715908) [find](https://scholar.google.com/scholar?q=Li+J%2C+Tao+CY%2C+Li+J%2C+Li+G%2C+Jin+Z%2C+Zhang+HZ%2C+Fang+Z%2C+Liu+F%2E+Large+language+model%2Daware+in%2Dcontext+learning+for+code+generation%2E+ACM+Trans%2E+on+Software+Engineering+and+Methodology%2C+2025%2C+34%287%29%3A+190%2E+%5Bdoi)
70. Wei J, Wei J, Tay Y, Tran D, Webson A, Lu YF, Chen XY, Liu HX, Huang D, Zhou D, Ma TY. Larger language models do in-context learning differently. arXiv:2303.03846, 2023. [find](https://scholar.google.com/scholar?q=Wei+J%2C+Wei+J%2C+Tay+Y%2C+Tran+D%2C+Webson+A%2C+Lu+YF%2C+Chen+XY%2C+Liu+HX%2C+Huang+D%2C+Zhou+D%2C+Ma+TY%2E+Larger+language+models+do+in%2Dcontext+learning+differently%2E+arXiv%3A2303%2E03846%2C+2023%2E) [our card](https://md.research.elamur.ai/free/papers/wei-2023-larger-language-models-context-learning/)
71. Huang D, Zhang JM, Luck M, Bu QW, Qing YH, Cui HM. AgentCoder: Multi-agent-based code generation with iterative testing and optimisation. arXiv:2312.13010, 2023. [find](https://scholar.google.com/scholar?q=Huang+D%2C+Zhang+JM%2C+Luck+M%2C+Bu+QW%2C+Qing+YH%2C+Cui+HM%2E+AgentCoder%3A+Multi%2Dagent%2Dbased+code+generation+with+iterative+testing+and+optimisation%2E+arXiv%3A2312%2E13010%2C+2023%2E)
72. Phan HN, Nguyen TN, Nguyen PX, Bui NDQ. HyperAgent: Generalist software engineering agents to solve coding tasks at scale. arXiv:2409.16299, 2024. — [arXiv:2409.16299](https://arxiv.org/abs/2409.16299) [find](https://scholar.google.com/scholar?q=Phan+HN%2C+Nguyen+TN%2C+Nguyen+PX%2C+Bui+NDQ%2E+HyperAgent%3A+Generalist+software+engineering+agents+to+solve+coding+tasks+at+scale%2E+arXiv%3A2409%2E16299%2C+2024%2E+%E2%80%94+%5BarXiv%3A2409%2E16299%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2409%2E16299)
73. Zhang KC, Zhang HZ, Li G, Li J, Li Z, Jin Z. ToolCoder: Teach code generation models to use API search tools. arXiv:2305.04032, 2023. — [arXiv:2305.04032](https://arxiv.org/abs/2305.04032) [find](https://scholar.google.com/scholar?q=Zhang+KC%2C+Zhang+HZ%2C+Li+G%2C+Li+J%2C+Li+Z%2C+Jin+Z%2E+ToolCoder%3A+Teach+code+generation+models+to+use+API+search+tools%2E+arXiv%3A2305%2E04032%2C+2023%2E+%E2%80%94+%5BarXiv%3A2305%2E04032%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2305%2E04032%29)
74. Phan HN, Phan HN, Nguyen TN, Bui NDQ. RepoHyper: Better context retrieval is all you need for repository-level code completion. arXiv:2403.06095, 2024. — [arXiv:2403.06095](https://arxiv.org/abs/2403.06095) [find](https://scholar.google.com/scholar?q=Phan+HN%2C+Phan+HN%2C+Nguyen+TN%2C+Bui+NDQ%2E+RepoHyper%3A+Better+context+retrieval+is+all+you+need+for+repository%2Dlevel+code+completion%2E+arXiv%3A2403%2E06095%2C+2024%2E+%E2%80%94+%5BarXiv%3A2403%2E06095%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2403%2E)
75. Madaan A, Tandon N, Gupta P, Hallinan S, Gao LY, Wiegreffe S, Alon U, Dziri N, Prabhumoye S, Yang YM, Gupta S, Majumder BP, Hermann K, Welleck S, Yazdanbakhsh A, Clark P. Self-Refine: Iterative refinement with self-feedback. In: Proc. of the 37th Int’l Conf. on Neural Information Processing Systems… [find](https://scholar.google.com/scholar?q=Madaan+A%2C+Tandon+N%2C+Gupta+P%2C+Hallinan+S%2C+Gao+LY%2C+Wiegreffe+S%2C+Alon+U%2C+Dziri+N%2C+Prabhumoye+S%2C+Yang+YM%2C+Gupta+S%2C+Majumder+BP%2C+Hermann+K%2C+Welleck+S%2C+Yazdanbakhsh+A%2C+Clark+P%2E+Self%2DRefine%3A+Iterative+refine)
76. Zhang KC, Li Z, Li J, Li G, Jin Z. Self-Edit: Fault-aware code editor for code generation. In: Proc. of the 61st Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Toronto: ACL, 2023. 769–787. [doi: 10.18653/v1/2023.acl-long.45] [find](https://scholar.google.com/scholar?q=Zhang+KC%2C+Li+Z%2C+Li+J%2C+Li+G%2C+Jin+Z%2E+Self%2DEdit%3A+Fault%2Daware+code+editor+for+code+generation%2E+In%3A+Proc%2E+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Vol%2E+1%3A+Long+Papers%29%2E+)
77. Wang XY, Chen YY, Yuan LF, Zhang YZ, Li YZ, Peng H, Ji H. Executable code actions elicit better LLM agents. In: Proc. of the 41st Int’l Conf. on Machine Learning (ICML). Vienna: OpenReview.net, 2024. 1–25. [find](https://scholar.google.com/scholar?q=Wang+XY%2C+Chen+YY%2C+Yuan+LF%2C+Zhang+YZ%2C+Li+YZ%2C+Peng+H%2C+Ji+H%2E+Executable+code+actions+elicit+better+LLM+agents%2E+In%3A+Proc%2E+of+the+41st+Int%E2%80%99l+Conf%2E+on+Machine+Learning+%28ICML%29%2E+Vienna%3A+OpenReview%2Enet%2C+2024%2E+) [our card](https://md.research.elamur.ai/free/papers/wang-2024-executable-code-actions-elicit-better/)
78. Huang T, Sun ZH, Jin Z, Li G, Lyu C. Knowledge-aware code generation with large language models. In: Proc. of the 32nd IEEE/ACM Int’l Conf. on Program Comprehension (ICPC). Lisbon: ACM, 2024. 52–63. [doi: 10.1145/3643916.3644418] — [doi:10.1145/3643916.3644418](https://doi.org/10.1145/3643916.3644418) [find](https://scholar.google.com/scholar?q=Huang+T%2C+Sun+ZH%2C+Jin+Z%2C+Li+G%2C+Lyu+C%2E+Knowledge%2Daware+code+generation+with+large+language+models%2E+In%3A+Proc%2E+of+the+32nd+IEEE%2FACM+Int%E2%80%99l+Conf%2E+on+Program+Comprehension+%28ICPC%29%2E+Lisbon%3A+ACM%2C+2024%2E+52%E2%80%9363%2E+%5B)
79. Gur I, Furuta H, Huang AV, Safdari M, Matsuo Y, Eck D, Faust A. A real-world WebAgent with planning, long context understanding, and program synthesis. In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.net, 2024. 1–28. [find](https://scholar.google.com/scholar?q=Gur+I%2C+Furuta+H%2C+Huang+AV%2C+Safdari+M%2C+Matsuo+Y%2C+Eck+D%2C+Faust+A%2E+A+real%2Dworld+WebAgent+with+planning%2C+long+context+understanding%2C+and+program+synthesis%2E+In%3A+Proc%2E+of+the+12th+Int%E2%80%99l+Conf%2E+on+Learning+Re)
80. Bairi R, Sonwane A, Kanade A, Vageesh DC, Iyer A, Parthasarathy S, Rajamani S, Ashok B, Shet S. CodePlan: Repository-level coding using LLMs and planning. Proc. of the ACM on Software Engineering, 2024, 1(FSE): 31. [doi: 10.1145/3643757] — [doi:10.1145/3643757](https://doi.org/10.1145/3643757) [find](https://scholar.google.com/scholar?q=Bairi+R%2C+Sonwane+A%2C+Kanade+A%2C+Vageesh+DC%2C+Iyer+A%2C+Parthasarathy+S%2C+Rajamani+S%2C+Ashok+B%2C+Shet+S%2E+CodePlan%3A+Repository%2Dlevel+coding+using+LLMs+and+planning%2E+Proc%2E+of+the+ACM+on+Software+Engineering%2C+202)
81. Dainese N, Merler M, Alakuijala M, Marttinen P. Generating code world models with large language models guided by Monte Carlo tree search. In: Proc. of the 38th Int’l Conf. on Neural Information Processing Systems (NeurIPS). Vancouver: Curran Associates Inc., 2024. 1933. [find](https://scholar.google.com/scholar?q=Dainese+N%2C+Merler+M%2C+Alakuijala+M%2C+Marttinen+P%2E+Generating+code+world+models+with+large+language+models+guided+by+Monte+Carlo+tree+search%2E+In%3A+Proc%2E+of+the+38th+Int%E2%80%99l+Conf%2E+on+Neural+Information+Proce)
82. Li JR, Le H, Zhou YB, Xiong CM, Savarese S, Sahoo D. CodeTree: Agent-guided tree search for code generation with large language models. In: Proc. of the 2025 Conf. of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1: Long Paper… [find](https://scholar.google.com/scholar?q=Li+JR%2C+Le+H%2C+Zhou+YB%2C+Xiong+CM%2C+Savarese+S%2C+Sahoo+D%2E+CodeTree%3A+Agent%2Dguided+tree+search+for+code+generation+with+large+language+models%2E+In%3A+Proc%2E+of+the+2025+Conf%2E+of+the+Nations+of+the+Americas+Chapt)
83. Ni ZY, Li YF, Yang N, Shen D, Lv P, Dong DX. Tree-of-Code: A tree-structured exploring framework for end-to-end code generation and execution in complex task handling. arXiv:2412.15305, 2024. — [arXiv:2412.15305](https://arxiv.org/abs/2412.15305) [find](https://scholar.google.com/scholar?q=Ni+ZY%2C+Li+YF%2C+Yang+N%2C+Shen+D%2C+Lv+P%2C+Dong+DX%2E+Tree%2Dof%2DCode%3A+A+tree%2Dstructured+exploring+framework+for+end%2Dto%2Dend+code+generation+and+execution+in+complex+task+handling%2E+arXiv%3A2412%2E15305%2C+2024%2E+%E2%80%94+%5BarXiv)
84. Han YW, Lyu C. Multi-stage guided code generation for large language models. Engineering Applications of Artificial Intelligence, 2025, 139: 109491. [doi: 10.1016/J.ENGAPPAI.2024.109491] — [doi:10.1016/j.engappai.2024.109491](https://doi.org/10.1016/j.engappai.2024.109491) [find](https://scholar.google.com/scholar?q=Han+YW%2C+Lyu+C%2E+Multi%2Dstage+guided+code+generation+for+large+language+models%2E+Engineering+Applications+of+Artificial+Intelligence%2C+2025%2C+139%3A+109491%2E+%5Bdoi%3A+10%2E1016%2FJ%2EENGAPPAI%2E2024%2E109491%5D+%E2%80%94+%5Bdoi%3A10%2E101)
85. Aggarwal V, Kamal O, Japesh A, Jin ZJ, Schölkopf B. DARS: Dynamic action re-sampling to enhance coding agent performance by adaptive tree traversal. In: Proc. of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Vienna: ACL, 2025. 19808–19855. [doi: 10.1… [find](https://scholar.google.com/scholar?q=Aggarwal+V%2C+Kamal+O%2C+Japesh+A%2C+Jin+ZJ%2C+Sch%C3%B6lkopf+B%2E+DARS%3A+Dynamic+action+re%2Dsampling+to+enhance+coding+agent+performance+by+adaptive+tree+traversal%2E+In%3A+Proc%2E+of+the+63rd+Annual+Meeting+of+the+Associa)
86. Ho CT, Ren HX, Khailany B. VerilogCoder: Autonomous verilog coding agents with graph-based planning and abstract syntax tree (AST)-based waveform tracing tool. In: Proc. of the 39th AAAI Conf. on Artificial Intelligence. Philadelphia: AAAI, 2025. 300–307. [doi: 10.1609/aaai.v39i1.32007] — [doi:10.1609/aaai.v39i1.32007](https://doi.org/10.1609/aaai.v39i1.32007) [find](https://scholar.google.com/scholar?q=Ho+CT%2C+Ren+HX%2C+Khailany+B%2E+VerilogCoder%3A+Autonomous+verilog+coding+agents+with+graph%2Dbased+planning+and+abstract+syntax+tree+%28AST%29%2Dbased+waveform+tracing+tool%2E+In%3A+Proc%2E+of+the+39th+AAAI+Conf%2E+on+Arti)
87. Zainullina K, Golubev A, Trofimova M, Polezhaev S, Badertdinov I, Litvintseva D, Karasik S, Fisin F, Skvortsov S, Nekrashevich M, Shevtsov A, Yangel B. Guided search strategies in non-serializable environments with applications to software engineering agents. In: Proc. of the 42nd Int’l Conf. on Mac… [find](https://scholar.google.com/scholar?q=Zainullina+K%2C+Golubev+A%2C+Trofimova+M%2C+Polezhaev+S%2C+Badertdinov+I%2C+Litvintseva+D%2C+Karasik+S%2C+Fisin+F%2C+Skvortsov+S%2C+Nekrashevich+M%2C+Shevtsov+A%2C+Yangel+B%2E+Guided+search+strategies+in+non%2Dserializable+env)
88. Jiang X, Dong YH, Tao YD, Liu HY, Jin Z, Li G. ROCODE: Integrating backtracking mechanism and program analysis in large language models for code generation. In: Proc. of the 47th Int’l Conf. on Software Engineering (ICSE). Ottawa: IEEE, 2025. 334–346. [doi: 10.1109/ICSE55347.2025.00133] — [doi:10.1109/icse55347.2025.00133](https://doi.org/10.1109/icse55347.2025.00133) [find](https://scholar.google.com/scholar?q=Jiang+X%2C+Dong+YH%2C+Tao+YD%2C+Liu+HY%2C+Jin+Z%2C+Li+G%2E+ROCODE%3A+Integrating+backtracking+mechanism+and+program+analysis+in+large+language+models+for+code+generation%2E+In%3A+Proc%2E+of+the+47th+Int%E2%80%99l+Conf%2E+on+Softwa)
89. Lu YF, Ye FH, Li J, Gao Q, Liu C, Luo HB, Du N, Li XL, Ren FL. CodeTool: Enhancing programmatic tool invocation of LLMs via process supervision. In: Proc. of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Vienna: ACL, 2025. 18287–18304. [doi: 10.18653… [find](https://scholar.google.com/scholar?q=Lu+YF%2C+Ye+FH%2C+Li+J%2C+Gao+Q%2C+Liu+C%2C+Luo+HB%2C+Du+N%2C+Li+XL%2C+Ren+FL%2E+CodeTool%3A+Enhancing+programmatic+tool+invocation+of+LLMs+via+process+supervision%2E+In%3A+Proc%2E+of+the+63rd+Annual+Meeting+of+the+Association)
90. Li J, Shi XJ, Zhang KC, Li G, Jin Z, Li L, Zhang HZ, Li J, Liu F, Zhang YW, Tao ZW, Dong YH, Zhu YQ, Tao CY. GraphCodeAgent: Dual graph-guided LLM agent for retrieval-augmented repo-level code generation. arXiv:2504.10046, 2025. — [arXiv:2504.10046](https://arxiv.org/abs/2504.10046) [find](https://scholar.google.com/scholar?q=Li+J%2C+Shi+XJ%2C+Zhang+KC%2C+Li+G%2C+Jin+Z%2C+Li+L%2C+Zhang+HZ%2C+Li+J%2C+Liu+F%2C+Zhang+YW%2C+Tao+ZW%2C+Dong+YH%2C+Zhu+YQ%2C+Tao+CY%2E+GraphCodeAgent%3A+Dual+graph%2Dguided+LLM+agent+for+retrieval%2Daugmented+repo%2Dlevel+code+generat)
91. Gupta T, Weihs L, Kembhavi A. CodeNav: Beyond tool-use to using real-world codebases with LLM agents. arXiv:2406.12276, 2024. — [arXiv:2406.12276](https://arxiv.org/abs/2406.12276) [find](https://scholar.google.com/scholar?q=Gupta+T%2C+Weihs+L%2C+Kembhavi+A%2E+CodeNav%3A+Beyond+tool%2Duse+to+using+real%2Dworld+codebases+with+LLM+agents%2E+arXiv%3A2406%2E12276%2C+2024%2E+%E2%80%94+%5BarXiv%3A2406%2E12276%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2406%2E12276%29)
92. Acharya M, Zhang YF, Leach K, Huang Y. Optimizing code runtime performance through context-aware retrieval-augmented generation. In: Proc. of the 33rd Int’l Conf. on Program Comprehension (ICPC). Ottawa: IEEE, 2025. 1–5. [doi: 10.1109/ICPC66645.2025.00028] — [doi:10.1109/icpc66645.2025.00028](https://doi.org/10.1109/icpc66645.2025.00028) [find](https://scholar.google.com/scholar?q=Acharya+M%2C+Zhang+YF%2C+Leach+K%2C+Huang+Y%2E+Optimizing+code+runtime+performance+through+context%2Daware+retrieval%2Daugmented+generation%2E+In%3A+Proc%2E+of+the+33rd+Int%E2%80%99l+Conf%2E+on+Program+Comprehension+%28ICPC%29%2E+Otta)
93. Athale M, Vaddina V. Knowledge graph based repository-level code generation. In: Proc. of the 2025 Int’l Workshop on Large Language Models for Code (LLM4Code). Ottawa: IEEE, 2025. 169–176. [doi: 10.1109/LLM4Code66737.2025.00026] — [doi:10.1109/llm4code66737.2025.00026](https://doi.org/10.1109/llm4code66737.2025.00026) [find](https://scholar.google.com/scholar?q=Athale+M%2C+Vaddina+V%2E+Knowledge+graph+based+repository%2Dlevel+code+generation%2E+In%3A+Proc%2E+of+the+2025+Int%E2%80%99l+Workshop+on+Large+Language+Models+for+Code+%28LLM4Code%29%2E+Ottawa%3A+IEEE%2C+2025%2E+169%E2%80%93176%2E+%5Bdoi%3A+10%2E11)
94. Zhang YL, Zhao XR, Wang ZZ, Yang CY, Wei JY, Wu TS. cAST: Enhancing code retrieval-augmented generation with structural chunking via abstract syntax tree. arXiv:2506.15655, 2025. [find](https://scholar.google.com/scholar?q=Zhang+YL%2C+Zhao+XR%2C+Wang+ZZ%2C+Yang+CY%2C+Wei+JY%2C+Wu+TS%2E+cAST%3A+Enhancing+code+retrieval%2Daugmented+generation+with+structural+chunking+via+abstract+syntax+tree%2E+arXiv%3A2506%2E15655%2C+2025%2E)
95. Lai Y, Lee S, Chen GJ, Poddar S, Hu MK, Pan DZ, Luo P. AnalogCoder: Analog circuit design via training-free code generation. In: Proc. of the 39th AAAI Conf. on Artificial Intelligence. Philadelphia: AAAI, 2025. 379–387. [doi: 10.1609/aaai.v39i1.32016] — [doi:10.1609/aaai.v39i1.32016](https://doi.org/10.1609/aaai.v39i1.32016) [find](https://scholar.google.com/scholar?q=Lai+Y%2C+Lee+S%2C+Chen+GJ%2C+Poddar+S%2C+Hu+MK%2C+Pan+DZ%2C+Luo+P%2E+AnalogCoder%3A+Analog+circuit+design+via+training%2Dfree+code+generation%2E+In%3A+Proc%2E+of+the+39th+AAAI+Conf%2E+on+Artificial+Intelligence%2E+Philadelphia%3A+)
96. Chang TY, Chen SZ, Fan GD, Feng ZY. A self-iteration code generation method based on large language models. In: Proc. of the 29th Int’l Conf. on Parallel and Distributed Systems (ICPADS). Ocean Flower Island: IEEE, 2023. 275–281. [doi: 10.1109/ICPADS60453.2023.00049] — [doi:10.1109/icpads60453.2023.00049](https://doi.org/10.1109/icpads60453.2023.00049) [find](https://scholar.google.com/scholar?q=Chang+TY%2C+Chen+SZ%2C+Fan+GD%2C+Feng+ZY%2E+A+self%2Diteration+code+generation+method+based+on+large+language+models%2E+In%3A+Proc%2E+of+the+29th+Int%E2%80%99l+Conf%2E+on+Parallel+and+Distributed+Systems+%28ICPADS%29%2E+Ocean+Flower)
97. Chen XY, Lin M, Schärli N, Zhou D. Teaching large language models to self-debug. In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.net, 2024. 1–80. [find](https://scholar.google.com/scholar?q=Chen+XY%2C+Lin+M%2C+Sch%C3%A4rli+N%2C+Zhou+D%2E+Teaching+large+language+models+to+self%2Ddebug%2E+In%3A+Proc%2E+of+the+12th+Int%E2%80%99l+Conf%2E+on+Learning+Representations+%28ICLR%29%2E+Vienna%3A+OpenReview%2Enet%2C+2024%2E+1%E2%80%9380%2E)
98. Olausson TX, Inala JP, Wang CL, Gao JF, Solar-Lezama A. Is Self-repair a silver bullet for code generation? In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.net, 2024. 1–49. [find](https://scholar.google.com/scholar?q=Olausson+TX%2C+Inala+JP%2C+Wang+CL%2C+Gao+JF%2C+Solar%2DLezama+A%2E+Is+Self%2Drepair+a+silver+bullet+for+code+generation%3F+In%3A+Proc%2E+of+the+12th+Int%E2%80%99l+Conf%2E+on+Learning+Representations+%28ICLR%29%2E+Vienna%3A+OpenReview%2Enet)
99. Jiang N, Li XP, Wang SQ, Zhou Q, Hossain SB, Ray B, Kumar V, Ma XF, Deoras A. LEDEX: Training LLMs to better self-debug and explain code. In: Proc. of the 38th Int’l Conf. on Neural Information Processing Systems (NeurIPS). Vancouver: Curran Associates Inc., 2024. 1120. [find](https://scholar.google.com/scholar?q=Jiang+N%2C+Li+XP%2C+Wang+SQ%2C+Zhou+Q%2C+Hossain+SB%2C+Ray+B%2C+Kumar+V%2C+Ma+XF%2C+Deoras+A%2E+LEDEX%3A+Training+LLMs+to+better+self%2Ddebug+and+explain+code%2E+In%3A+Proc%2E+of+the+38th+Int%E2%80%99l+Conf%2E+on+Neural+Information+Proces)
100. Tao W, Zhou YC, Wang YL, Zhang WQ, Zhang HY, Cheng Y. MAGIS: LLM-based multi-agent framework for GitHub issue ReSolution. In: Proc. of the 38th Int’l Conf. on Neural Information Processing Systems (NeurIPS). Vancouver: Curran Associates Inc., 2024. 1647. [find](https://scholar.google.com/scholar?q=Tao+W%2C+Zhou+YC%2C+Wang+YL%2C+Zhang+WQ%2C+Zhang+HY%2C+Cheng+Y%2E+MAGIS%3A+LLM%2Dbased+multi%2Dagent+framework+for+GitHub+issue+ReSolution%2E+In%3A+Proc%2E+of+the+38th+Int%E2%80%99l+Conf%2E+on+Neural+Information+Processing+Systems+%28Ne)
101. Zhang H, Cheng W, Wu YH, Hu W. A pair programming framework for code generation via multi-plan exploration and feedback-driven refinement. In: Proc. of the 39th IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). Sacramento: ACM, 2024. 1319–1331. [doi: 10.1145/3691620.3695506] — [doi:10.1145/3691620.3695506](https://doi.org/10.1145/3691620.3695506) [find](https://scholar.google.com/scholar?q=Zhang+H%2C+Cheng+W%2C+Wu+YH%2C+Hu+W%2E+A+pair+programming+framework+for+code+generation+via+multi%2Dplan+exploration+and+feedback%2Ddriven+refinement%2E+In%3A+Proc%2E+of+the+39th+IEEE%2FACM+Int%E2%80%99l+Conf%2E+on+Automated+Softw)
102. Lin F, Kim DJ, Chen TH. SOEN-101: Code generation by emulating software process models using large language model agents. In: Proc. of the 47th Int’l Conf. on Software Engineering (ICSE). Ottawa: IEEE, 2025. 1527–1539. [doi: 10.1109/ICSE55347.2025.00140] — [doi:10.1109/icse55347.2025.00140](https://doi.org/10.1109/icse55347.2025.00140) [find](https://scholar.google.com/scholar?q=Lin+F%2C+Kim+DJ%2C+Chen+TH%2E+SOEN%2D101%3A+Code+generation+by+emulating+software+process+models+using+large+language+model+agents%2E+In%3A+Proc%2E+of+the+47th+Int%E2%80%99l+Conf%2E+on+Software+Engineering+%28ICSE%29%2E+Ottawa%3A+IEEE)
103. Zhao YJ, Zhang HJ, Huang HX, Yu ZM, Zhao JS. MAGE: A multi-agent engine for automated RTL code generation. In: Proc. of the 62nd ACM/IEEE Design Automation Conf. (DAC). San Francisco: IEEE, 2025. 1–7. [doi: 10.1109/DAC63849.2025.11133191] — [doi:10.1109/dac63849.2025.11133191](https://doi.org/10.1109/dac63849.2025.11133191) [find](https://scholar.google.com/scholar?q=Zhao+YJ%2C+Zhang+HJ%2C+Huang+HX%2C+Yu+ZM%2C+Zhao+JS%2E+MAGE%3A+A+multi%2Dagent+engine+for+automated+RTL+code+generation%2E+In%3A+Proc%2E+of+the+62nd+ACM%2FIEEE+Design+Automation+Conf%2E+%28DAC%29%2E+San+Francisco%3A+IEEE%2C+2025%2E+1%E2%80%937%2E)
104. Islam MA, Ali ME, Parvez MR. MapCoder: Multi-agent code generation for competitive problem solving. In: Proc. of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). Bangkok: ACL, 2024. 4912–4944. [doi: 10.18653/v1/2024.acl-long.269] [find](https://scholar.google.com/scholar?q=Islam+MA%2C+Ali+ME%2C+Parvez+MR%2E+MapCoder%3A+Multi%2Dagent+code+generation+for+competitive+problem+solving%2E+In%3A+Proc%2E+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Vol%2E+1%3A+Long+)
105. Nunez A, Islam NT, Jha SK, Najafirad P. AutoSafeCoder: A multi-agent framework for securing LLM code generation through static analysis and fuzz testing. arXiv:2409.10737, 2024. — [arXiv:2409.10737](https://arxiv.org/abs/2409.10737) [find](https://scholar.google.com/scholar?q=Nunez+A%2C+Islam+NT%2C+Jha+SK%2C+Najafirad+P%2E+AutoSafeCoder%3A+A+multi%2Dagent+framework+for+securing+LLM+code+generation+through+static+analysis+and+fuzz+testing%2E+arXiv%3A2409%2E10737%2C+2024%2E+%E2%80%94+%5BarXiv%3A2409%2E10737%5D%28h)
106. Rahman A, Cvetkovic V, Reece K, Walters A, Hassan Y, Tummeti A, Torres B, Cooney D, Ellis M, Nikolopoulos DS. MARCO: A multi-agent system for optimizing HPC code generation using large language models. arXiv:2505.03906, 2025. — [arXiv:2505.03906](https://arxiv.org/abs/2505.03906) [find](https://scholar.google.com/scholar?q=Rahman+A%2C+Cvetkovic+V%2C+Reece+K%2C+Walters+A%2C+Hassan+Y%2C+Tummeti+A%2C+Torres+B%2C+Cooney+D%2C+Ellis+M%2C+Nikolopoulos+DS%2E+MARCO%3A+A+multi%2Dagent+system+for+optimizing+HPC+code+generation+using+large+language+models)
107. Liu SW, Fang JY, Zhou H, Wang YX, Meng ZQ. SEW: Self-evolving agentic workflows for automated code generation. arXiv:2505.18646, 2025. — [arXiv:2505.18646](https://arxiv.org/abs/2505.18646) [find](https://scholar.google.com/scholar?q=Liu+SW%2C+Fang+JY%2C+Zhou+H%2C+Wang+YX%2C+Meng+ZQ%2E+SEW%3A+Self%2Devolving+agentic+workflows+for+automated+code+generation%2E+arXiv%3A2505%2E18646%2C+2025%2E+%E2%80%94+%5BarXiv%3A2505%2E18646%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2505%2E18646%29)
108. Hu Y, Cai YZ, Du YX, Zhu XY, Liu XR, Yu ZJ, Hou YC, Tang S, Chen SH. Self-evolving multi-agent collaboration networks for software development. In: Proc. of the 13th Int’l Conf. on Learning Representations (ICLR). Singapore: OpenReview.net, 2025. 1–33. [find](https://scholar.google.com/scholar?q=Hu+Y%2C+Cai+YZ%2C+Du+YX%2C+Zhu+XY%2C+Liu+XR%2C+Yu+ZJ%2C+Hou+YC%2C+Tang+S%2C+Chen+SH%2E+Self%2Devolving+multi%2Dagent+collaboration+networks+for+software+development%2E+In%3A+Proc%2E+of+the+13th+Int%E2%80%99l+Conf%2E+on+Learning+Representa)
109. Holt S, Luyten MR, van der Schaar M. L2MAC: Large language model automatic computer for extensive code generation. In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.net, 2024. 1–61. [find](https://scholar.google.com/scholar?q=Holt+S%2C+Luyten+MR%2C+van+der+Schaar+M%2E+L2MAC%3A+Large+language+model+automatic+computer+for+extensive+code+generation%2E+In%3A+Proc%2E+of+the+12th+Int%E2%80%99l+Conf%2E+on+Learning+Representations+%28ICLR%29%2E+Vienna%3A+OpenRev)
110. Li YL, Li JD, Wang Q, Yang ML, Kong H, Wang SS. Cogito, ergo sum: A neurobiologically-inspired cognition-memory-growth system for code generation. arXiv:2501.18653, 2025. — [arXiv:2501.18653](https://arxiv.org/abs/2501.18653) [find](https://scholar.google.com/scholar?q=Li+YL%2C+Li+JD%2C+Wang+Q%2C+Yang+ML%2C+Kong+H%2C+Wang+SS%2E+Cogito%2C+ergo+sum%3A+A+neurobiologically%2Dinspired+cognition%2Dmemory%2Dgrowth+system+for+code+generation%2E+arXiv%3A2501%2E18653%2C+2025%2E+%E2%80%94+%5BarXiv%3A2501%2E18653%5D%28https%3A%2F%2F)
111. Qi DR, Miao ZJ, Wang JN. CleanAgent: Automating data standardization with LLM-based agents. arXiv:2403.08291, 2024. — [arXiv:2403.08291](https://arxiv.org/abs/2403.08291) [find](https://scholar.google.com/scholar?q=Qi+DR%2C+Miao+ZJ%2C+Wang+JN%2E+CleanAgent%3A+Automating+data+standardization+with+LLM%2Dbased+agents%2E+arXiv%3A2403%2E08291%2C+2024%2E+%E2%80%94+%5BarXiv%3A2403%2E08291%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2403%2E08291%29)
112. Ma YW, Cao RY, Cao YC, Zhang Y, Chen J, Liu YB, Liu YC, Li BH, Huang F, Li YB. Lingma SWE-GPT: An open development-process-centric language model for automated software improvement. arXiv:2411.00622, 2024. — [arXiv:2411.00622](https://arxiv.org/abs/2411.00622) [find](https://scholar.google.com/scholar?q=Ma+YW%2C+Cao+RY%2C+Cao+YC%2C+Zhang+Y%2C+Chen+J%2C+Liu+YB%2C+Liu+YC%2C+Li+BH%2C+Huang+F%2C+Li+YB%2E+Lingma+SWE%2DGPT%3A+An+open+development%2Dprocess%2Dcentric+language+model+for+automated+software+improvement%2E+arXiv%3A2411%2E00622%2C+)
113. Guo XH, Wang XY, Chen YY, Li S, Han C, Li ML, Ji H. SyncMind: Measuring agent out-of-sync recovery in collaborative software engineering. In: Proc. of the 42nd Int’l Conf. on Machine Learning (ICML). Vancouver: OpenReview.net, 2025. 1–74. [find](https://scholar.google.com/scholar?q=Guo+XH%2C+Wang+XY%2C+Chen+YY%2C+Li+S%2C+Han+C%2C+Li+ML%2C+Ji+H%2E+SyncMind%3A+Measuring+agent+out%2Dof%2Dsync+recovery+in+collaborative+software+engineering%2E+In%3A+Proc%2E+of+the+42nd+Int%E2%80%99l+Conf%2E+on+Machine+Learning+%28ICML%29%2E+)
114. Zhou A, Yan K, Shlapentokh-Rothman M, Wang HH, Wang YX. Language agent tree search unifies reasoning, acting, and planning in language models. In: Proc. of the 41st Int’l Conf. on Machine Learning (ICML). Vienna: OpenReview.net, 2024. 1–23. [find](https://scholar.google.com/scholar?q=Zhou+A%2C+Yan+K%2C+Shlapentokh%2DRothman+M%2C+Wang+HH%2C+Wang+YX%2E+Language+agent+tree+search+unifies+reasoning%2C+acting%2C+and+planning+in+language+models%2E+In%3A+Proc%2E+of+the+41st+Int%E2%80%99l+Conf%2E+on+Machine+Learning+%28IC)
115. Jin DM, Jin Z, Chen XH, Wang CH. MARE: Multi-agents collaboration framework for requirements engineering. arXiv:2405.03256, 2024. — [arXiv:2405.03256](https://arxiv.org/abs/2405.03256) [find](https://scholar.google.com/scholar?q=Jin+DM%2C+Jin+Z%2C+Chen+XH%2C+Wang+CH%2E+MARE%3A+Multi%2Dagents+collaboration+framework+for+requirements+engineering%2E+arXiv%3A2405%2E03256%2C+2024%2E+%E2%80%94+%5BarXiv%3A2405%2E03256%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2405%2E03256%29)
116. Jiang X, Dong YH, Fan ZY, Jin Z, Jiao WP, Li G. Exploring data-efficient adaptation of large language models for code generation. ACM Trans. Software Engineering Methodology, 2025. [doi: 10.1145/3772721] — [doi:10.1145/3772721](https://doi.org/10.1145/3772721) [find](https://scholar.google.com/scholar?q=Jiang+X%2C+Dong+YH%2C+Fan+ZY%2C+Jin+Z%2C+Jiao+WP%2C+Li+G%2E+Exploring+data%2Defficient+adaptation+of+large+language+models+for+code+generation%2E+ACM+Trans%2E+Software+Engineering+Methodology%2C+2025%2E+%5Bdoi%3A+10%2E1145%2F37727)
117. Xu YH, Su HJ, Xing C, Mi BY, Liu Q, Shi WJ, Hui BY, Zhou F, Liu YT, Xie TB, Cheng ZJ, Zhao SH, Kong LP, Wang BL, Xiong CM, Yu T. Lemur: Harmonizing natural language and code for language agents. In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.net, 2024. 1–21. [find](https://scholar.google.com/scholar?q=Xu+YH%2C+Su+HJ%2C+Xing+C%2C+Mi+BY%2C+Liu+Q%2C+Shi+WJ%2C+Hui+BY%2C+Zhou+F%2C+Liu+YT%2C+Xie+TB%2C+Cheng+ZJ%2C+Zhao+SH%2C+Kong+LP%2C+Wang+BL%2C+Xiong+CM%2C+Yu+T%2E+Lemur%3A+Harmonizing+natural+language+and+code+for+language+agents%2E+In%3A+P)
118. Islam MA, Ali ME, Parvez MR. CodeSim: Multi-agent code generation and problem solving through simulation-driven planning and debugging. In: Findings of the Association for Computational Linguistics: NAACL 2025. Albuquerque: ACL, 2025. 5113–5139. [doi: 10.18653/v1/2025.findings-naacl.285] [find](https://scholar.google.com/scholar?q=Islam+MA%2C+Ali+ME%2C+Parvez+MR%2E+CodeSim%3A+Multi%2Dagent+code+generation+and+problem+solving+through+simulation%2Ddriven+planning+and+debugging%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+N)
119. Nguyen MH, Chau TP, Nguyen PX, Bui NDQ. AgileCoder: Dynamic collaborative agents for software development based on agile methodology. In: Proc. of the 2nd Int’l Conf. on AI Foundation Models and Software Engineering (FORGE). Ottawa: IEEE, 2025. 156–167. [doi: 10.1109/Forge66646.2025.00026] — [doi:10.1109/forge66646.2025.00026](https://doi.org/10.1109/forge66646.2025.00026) [find](https://scholar.google.com/scholar?q=Nguyen+MH%2C+Chau+TP%2C+Nguyen+PX%2C+Bui+NDQ%2E+AgileCoder%3A+Dynamic+collaborative+agents+for+software+development+based+on+agile+methodology%2E+In%3A+Proc%2E+of+the+2nd+Int%E2%80%99l+Conf%2E+on+AI+Foundation+Models+and+Softw)
120. Chen DK, Wang HB, Huo YH, Li YZ, Zhang HY. GameGPT: Multi-agent collaborative framework for game development. arXiv:2310.08067, 2023. [find](https://scholar.google.com/scholar?q=Chen+DK%2C+Wang+HB%2C+Huo+YH%2C+Li+YZ%2C+Zhang+HY%2E+GameGPT%3A+Multi%2Dagent+collaborative+framework+for+game+development%2E+arXiv%3A2310%2E08067%2C+2023%2E)
121. Zan DG, Yu AL, Liu W, Chen D, Shen B, Li W, Yao YF, Gong YS, Chen XL, Guan B, Yang ZG, Wang YJ, Wang QX, Cui LZ. CodeS: Natural language to code repository via multi-layer sketch. arXiv:2403.16443, 2024. [find](https://scholar.google.com/scholar?q=Zan+DG%2C+Yu+AL%2C+Liu+W%2C+Chen+D%2C+Shen+B%2C+Li+W%2C+Yao+YF%2C+Gong+YS%2C+Chen+XL%2C+Guan+B%2C+Yang+ZG%2C+Wang+YJ%2C+Wang+QX%2C+Cui+LZ%2E+CodeS%3A+Natural+language+to+code+repository+via+multi%2Dlayer+sketch%2E+arXiv%3A2403%2E16443%2C+20)
122. Zhang YT, Ruan HF, Fan ZY, Roychoudhury A. AutoCodeRover: Autonomous program improvement. In: Proc. of the 33rd ACM SIGSOFT Int’l Symp. on Software Testing and Analysis (ISSTA). Vienna: ACM, 2024. 1592–1604. [doi: 10.1145/3650212.3680384] — [doi:10.1145/3650212.3680384](https://doi.org/10.1145/3650212.3680384) [find](https://scholar.google.com/scholar?q=Zhang+YT%2C+Ruan+HF%2C+Fan+ZY%2C+Roychoudhury+A%2E+AutoCodeRover%3A+Autonomous+program+improvement%2E+In%3A+Proc%2E+of+the+33rd+ACM+SIGSOFT+Int%E2%80%99l+Symp%2E+on+Software+Testing+and+Analysis+%28ISSTA%29%2E+Vienna%3A+ACM%2C+2024%2E+159)
123. Yang J, Jimenez CE, Wettig A, Lieret K, Yao SY, Narasimhan K, Press O. SWE-agent: Agent-computer interfaces enable automated software engineering. In: Proc. of the 38th Int’l Conf. on Neural Information Processing Systems (NeurIPS). Vancouver: Curran Associates Inc., 2024. 1601. [find](https://scholar.google.com/scholar?q=Yang+J%2C+Jimenez+CE%2C+Wettig+A%2C+Lieret+K%2C+Yao+SY%2C+Narasimhan+K%2C+Press+O%2E+SWE%2Dagent%3A+Agent%2Dcomputer+interfaces+enable+automated+software+engineering%2E+In%3A+Proc%2E+of+the+38th+Int%E2%80%99l+Conf%2E+on+Neural+Informati) [our card](https://md.research.elamur.ai/free/papers/yang-2024-swe-agent-agent-computer-interfaces/)
124. Antoniades A, Örwall A, Zhang KX, Xie YX, Goyal A, Wang WY. SWE-search: Enhancing software agents with Monte Carlo tree search and iterative refinement. In: Proc. of the 13th Int’l Conf. on Learning Representations (ICLR). Singapore: OpenReview.net, 2025. 1–31. [find](https://scholar.google.com/scholar?q=Antoniades+A%2C+%C3%96rwall+A%2C+Zhang+KX%2C+Xie+YX%2C+Goyal+A%2C+Wang+WY%2E+SWE%2Dsearch%3A+Enhancing+software+agents+with+Monte+Carlo+tree+search+and+iterative+refinement%2E+In%3A+Proc%2E+of+the+13th+Int%E2%80%99l+Conf%2E+on+Learning+R)
125. Bouzenia I, Devanbu P, Pradel M. RepairAgent: An autonomous, LLM-based agent for program repair. In: Proc. of the 47th Int’l Conf. on Software Engineering (ICSE). Ottawa: IEEE, 2025. 2188–2200. [doi: 10.1109/ICSE55347.2025.00157] — [doi:10.1109/icse55347.2025.00157](https://doi.org/10.1109/icse55347.2025.00157) [find](https://scholar.google.com/scholar?q=Bouzenia+I%2C+Devanbu+P%2C+Pradel+M%2E+RepairAgent%3A+An+autonomous%2C+LLM%2Dbased+agent+for+program+repair%2E+In%3A+Proc%2E+of+the+47th+Int%E2%80%99l+Conf%2E+on+Software+Engineering+%28ICSE%29%2E+Ottawa%3A+IEEE%2C+2025%2E+2188%E2%80%932200%2E+%5Bdoi%3A+)
126. Cen JP, Liu JX, Li ZX, Wang JJ. SQLFixAgent: Towards semantic-accurate text-to-SQL parsing via consistency-enhanced multi-agent collaboration. In: Proc. of the 39th AAAI Conf. on Artificial Intelligence. Philadelphia: AAAI, 2025. 49–57. [doi: 10.1609/aaai.v39i1.31979] — [doi:10.1609/aaai.v39i1.31979](https://doi.org/10.1609/aaai.v39i1.31979) [find](https://scholar.google.com/scholar?q=Cen+JP%2C+Liu+JX%2C+Li+ZX%2C+Wang+JJ%2E+SQLFixAgent%3A+Towards+semantic%2Daccurate+text%2Dto%2DSQL+parsing+via+consistency%2Denhanced+multi%2Dagent+collaboration%2E+In%3A+Proc%2E+of+the+39th+AAAI+Conf%2E+on+Artificial+Intelligen)
127. Yu ZM, Zhang HJ, Zhao YJ, Huang HX, Yao M, Ding K, Zhao JS. OrcaLoca: An LLM agent framework for software issue localization. In: Proc. of the 42nd Int’l Conf. on Machine Learning (ICML). Vancouver: OpenReview.net, 2025. 1–21. [find](https://scholar.google.com/scholar?q=Yu+ZM%2C+Zhang+HJ%2C+Zhao+YJ%2C+Huang+HX%2C+Yao+M%2C+Ding+K%2C+Zhao+JS%2E+OrcaLoca%3A+An+LLM+agent+framework+for+software+issue+localization%2E+In%3A+Proc%2E+of+the+42nd+Int%E2%80%99l+Conf%2E+on+Machine+Learning+%28ICML%29%2E+Vancouver%3A+O)
128. Li HW, Tang YH, Wang SQ, Guo WB. PatchPilot: A stable and cost-efficient agentic patching framework. arXiv:2502.02747, 2025. — [arXiv:2502.02747](https://arxiv.org/abs/2502.02747) [find](https://scholar.google.com/scholar?q=Li+HW%2C+Tang+YH%2C+Wang+SQ%2C+Guo+WB%2E+PatchPilot%3A+A+stable+and+cost%2Defficient+agentic+patching+framework%2E+arXiv%3A2502%2E02747%2C+2025%2E+%E2%80%94+%5BarXiv%3A2502%2E02747%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2502%2E02747%29)
129. Ma YW, Li YB, Dong YH, Jiang X, Cao RY, Chen J, Huang F, Li BH. Thinking longer, not larger: Enhancing software engineering agents via scaling test-time compute. arXiv:2503.23803, 2025. — [arXiv:2503.23803](https://arxiv.org/abs/2503.23803) [find](https://scholar.google.com/scholar?q=Ma+YW%2C+Li+YB%2C+Dong+YH%2C+Jiang+X%2C+Cao+RY%2C+Chen+J%2C+Huang+F%2C+Li+BH%2E+Thinking+longer%2C+not+larger%3A+Enhancing+software+engineering+agents+via+scaling+test%2Dtime+compute%2E+arXiv%3A2503%2E23803%2C+2025%2E+%E2%80%94+%5BarXiv%3A2503%2E)
130. Ye H, Yang AZH, Hu C, Wang YL, Zhang T, Le Goues C. AdverIntent-agent: Adversarial reasoning for repair based on inferred program intent. Proc. of the ACM on Software Engineering, 2025, 2(ISSTA): ISSTA062. [doi: 10.1145/3728939] — [doi:10.1145/3728939](https://doi.org/10.1145/3728939) [find](https://scholar.google.com/scholar?q=Ye+H%2C+Yang+AZH%2C+Hu+C%2C+Wang+YL%2C+Zhang+T%2C+Le+Goues+C%2E+AdverIntent%2Dagent%3A+Adversarial+reasoning+for+repair+based+on+inferred+program+intent%2E+Proc%2E+of+the+ACM+on+Software+Engineering%2C+2025%2C+2%28ISSTA%29%3A+ISST)
131. Sohrabizadeh A, Song JL, Liu MJ, Roy R, Lee C, Raiman J, Catanzaro B. Nemotron-CORTEXA: Enhancing LLM agents for software engineering tasks via improved localization and solution diversity. In: Proc. of the 42nd Int’l Conf. on Machine Learning (ICML). Vancouver: OpenReview.net, 2025. 1–16. [find](https://scholar.google.com/scholar?q=Sohrabizadeh+A%2C+Song+JL%2C+Liu+MJ%2C+Roy+R%2C+Lee+C%2C+Raiman+J%2C+Catanzaro+B%2E+Nemotron%2DCORTEXA%3A+Enhancing+LLM+agents+for+software+engineering+tasks+via+improved+localization+and+solution+diversity%2E+In%3A+Proc%2E+)
132. Hu YX, Wang X, Wang YC, Zhang Y, Guo SY, Chen CY, Wang X, Zhou YF. AUITestAgent: Automatic requirements oriented GUI function testing. arXiv:2407.09018, 2024. — [arXiv:2407.09018](https://arxiv.org/abs/2407.09018) [find](https://scholar.google.com/scholar?q=Hu+YX%2C+Wang+X%2C+Wang+YC%2C+Zhang+Y%2C+Guo+SY%2C+Chen+CY%2C+Wang+X%2C+Zhou+YF%2E+AUITestAgent%3A+Automatic+requirements+oriented+GUI+function+testing%2E+arXiv%3A2407%2E09018%2C+2024%2E+%E2%80%94+%5BarXiv%3A2407%2E09018%5D%28https%3A%2F%2Farxiv%2Eorg%2Fab)
133. Liu KB, Chen ZP, Liu YY, Zhang JM, Harman M, Han YD, Ma Y, Dong YH, Li G, Huang G. LLM-powered test case generation for detecting bugs in plausible programs. In: Proc. of the 63rd Annual Meeting of Association for Computational Linguistics (Vol. 1: Long Papers). Vienna: ACL, 2025. 430–440. [doi: 10.… [find](https://scholar.google.com/scholar?q=Liu+KB%2C+Chen+ZP%2C+Liu+YY%2C+Zhang+JM%2C+Harman+M%2C+Han+YD%2C+Ma+Y%2C+Dong+YH%2C+Li+G%2C+Huang+G%2E+LLM%2Dpowered+test+case+generation+for+detecting+bugs+in+plausible+programs%2E+In%3A+Proc%2E+of+the+63rd+Annual+Meeting+of+As)
134. Fraser G, Arcuri A. EvoSuite: On the challenges of test case generation in the real world. In: Proc. of the 6th IEEE Int’l Conf. on Software Testing, Verification and Validation. Luxembourg: IEEE, 2013. 362–369. [doi: 10.1109/ICST.2013.51] — [doi:10.1109/icst.2013.51](https://doi.org/10.1109/icst.2013.51) [find](https://scholar.google.com/scholar?q=Fraser+G%2C+Arcuri+A%2E+EvoSuite%3A+On+the+challenges+of+test+case+generation+in+the+real+world%2E+In%3A+Proc%2E+of+the+6th+IEEE+Int%E2%80%99l+Conf%2E+on+Software+Testing%2C+Verification+and+Validation%2E+Luxembourg%3A+IEEE%2C+201)
135. Bazalii M. Building AI agents to automate software test case creation. 2024. [https://developer.nvidia.com/blog/building-ai-agents-to-automate-software-test-case-creation/](https://developer.nvidia.com/blog/building-ai-agents-to-automate-software-test-case-creation/) [↗](https://developer.nvidia.com/blog/building-ai-agents-to-automate-software-test-case-creation/)
136. Jiang ZY, Schmidt D, Srikanth D, Xu DX, Kaplan I, Jacenko D, Wu YX. AIDE: AI-driven exploration in the space of code. arXiv:2502.13138, 2025. [find](https://scholar.google.com/scholar?q=Jiang+ZY%2C+Schmidt+D%2C+Srikanth+D%2C+Xu+DX%2C+Kaplan+I%2C+Jacenko+D%2C+Wu+YX%2E+AIDE%3A+AI%2Ddriven+exploration+in+the+space+of+code%2E+arXiv%3A2502%2E13138%2C+2025%2E)
137. Pomian D, Bellur A, Dilhara M, Kurbatova Z, Bogomolov E, Sokolov A, Bryksin T, Dig D. EM-assist: Safe automated extractmethod refactoring with LLMs. In: Proc. of the 32nd Int’l Conf. on the Foundations of Software Engineering (FSE). Porto de Galinhas: ACM, 2024. 582–586. [doi: 10.1145/3663529.366380… — [doi:10.1145/3663529.3663803](https://doi.org/10.1145/3663529.3663803) [find](https://scholar.google.com/scholar?q=Pomian+D%2C+Bellur+A%2C+Dilhara+M%2C+Kurbatova+Z%2C+Bogomolov+E%2C+Sokolov+A%2C+Bryksin+T%2C+Dig+D%2E+EM%2Dassist%3A+Safe+automated+extractmethod+refactoring+with+LLMs%2E+In%3A+Proc%2E+of+the+32nd+Int%E2%80%99l+Conf%2E+on+the+Foundation)
138. Wu D, Mu FW, Shi L, Guo ZQ, Liu K, Zhuang WG, Zhong YQ, Zhang L. iSMELL: Assembling LLMs with expert toolsets for code smell detection and refactoring. In: Proc. of the 39th Int’l Conf. on Automated Software Engineering (ICASE). Sacramento: ACM, 2024. 1345–1357. [doi: 10.1145/3691620.3695508] — [doi:10.1145/3691620.3695508](https://doi.org/10.1145/3691620.3695508) [find](https://scholar.google.com/scholar?q=Wu+D%2C+Mu+FW%2C+Shi+L%2C+Guo+ZQ%2C+Liu+K%2C+Zhuang+WG%2C+Zhong+YQ%2C+Zhang+L%2E+iSMELL%3A+Assembling+LLMs+with+expert+toolsets+for+code+smell+detection+and+refactoring%2E+In%3A+Proc%2E+of+the+39th+Int%E2%80%99l+Conf%2E+on+Automated+S)
139. Siddeeq S, Rasheed Z, Sami MA, Hasan M, Waseem M, Rasku J, Saari M, Kai-Kemell K, Abrahamsson P. Distributed approach to Haskell based applications refactoring with LLMs based multi-agent systems. arXiv:2502.07928, 2025. — [arXiv:2502.07928](https://arxiv.org/abs/2502.07928) [find](https://scholar.google.com/scholar?q=Siddeeq+S%2C+Rasheed+Z%2C+Sami+MA%2C+Hasan+M%2C+Waseem+M%2C+Rasku+J%2C+Saari+M%2C+Kai%2DKemell+K%2C+Abrahamsson+P%2E+Distributed+approach+to+Haskell+based+applications+refactoring+with+LLMs+based+multi%2Dagent+systems%2E+arX)
140. Fakhoury S, Naik A, Sakkas G, Chakraborty S, Lahiri SK. LLM-based test-driven interactive code generation: User study and empirical evaluation. IEEE Trans. on Software Engineering, 2024, 50(9): 2254–2268. [doi: 10.1109/TSE.2024.3428972] — [doi:10.1109/tse.2024.3428972](https://doi.org/10.1109/tse.2024.3428972) [find](https://scholar.google.com/scholar?q=Fakhoury+S%2C+Naik+A%2C+Sakkas+G%2C+Chakraborty+S%2C+Lahiri+SK%2E+LLM%2Dbased+test%2Ddriven+interactive+code+generation%3A+User+study+and+empirical+evaluation%2E+IEEE+Trans%2E+on+Software+Engineering%2C+2024%2C+50%289%29%3A+2254%E2%80%932)
141. Jia HX, Morris R, Ye H, Sarro F, Mechtaev S. Automated repair of ambiguous natural language requirements. arXiv:2505.07270, 2025. — [arXiv:2505.07270](https://arxiv.org/abs/2505.07270) [find](https://scholar.google.com/scholar?q=Jia+HX%2C+Morris+R%2C+Ye+H%2C+Sarro+F%2C+Mechtaev+S%2E+Automated+repair+of+ambiguous+natural+language+requirements%2E+arXiv%3A2505%2E07270%2C+2025%2E+%E2%80%94+%5BarXiv%3A2505%2E07270%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2505%2E07270%29)
142. Vijayvargiya S, Zhou XH, Yerukola A, Sap M, Neubig G. Interactive agents to overcome ambiguity in software engineering. arXiv:2502.13069, 2025. — [arXiv:2502.13069](https://arxiv.org/abs/2502.13069) [find](https://scholar.google.com/scholar?q=Vijayvargiya+S%2C+Zhou+XH%2C+Yerukola+A%2C+Sap+M%2C+Neubig+G%2E+Interactive+agents+to+overcome+ambiguity+in+software+engineering%2E+arXiv%3A2502%2E13069%2C+2025%2E+%E2%80%94+%5BarXiv%3A2502%2E13069%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2502%2E13069%29)
143. González EA, Rothkopf R, Lerner S, Polikarpova N. HiLDe: Intentional code generation via human-in-the-loop decoding. arXiv:2505.22906, 2025. — [arXiv:2505.22906](https://arxiv.org/abs/2505.22906) [find](https://scholar.google.com/scholar?q=Gonz%C3%A1lez+EA%2C+Rothkopf+R%2C+Lerner+S%2C+Polikarpova+N%2E+HiLDe%3A+Intentional+code+generation+via+human%2Din%2Dthe%2Dloop+decoding%2E+arXiv%3A2505%2E22906%2C+2025%2E+%E2%80%94+%5BarXiv%3A2505%2E22906%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2505%2E22906%29)
144. Austin J, Odena A, Nye M, Bosma M, Michalewski H, Dohan D, Jiang E, Cai C, Terry M, Le Q, Sutton C. Program synthesis with large language models. arXiv:2108.07732, 2021. [find](https://scholar.google.com/scholar?q=Austin+J%2C+Odena+A%2C+Nye+M%2C+Bosma+M%2C+Michalewski+H%2C+Dohan+D%2C+Jiang+E%2C+Cai+C%2C+Terry+M%2C+Le+Q%2C+Sutton+C%2E+Program+synthesis+with+large+language+models%2E+arXiv%3A2108%2E07732%2C+2021%2E) [our card](https://md.research.elamur.ai/free/papers/austin-2021-program-synthesis-large-language-models/)
145. Dong YH, Ding JZ, Jiang X, Li G, Li Z, Jin Z. CodeScore: Evaluating code generation by learning code execution. ACM Trans. on Software Engineering and Methodology, 2025, 34(3): 77. [doi: 10.1145/3695991] — [doi:10.1145/3695991](https://doi.org/10.1145/3695991) [find](https://scholar.google.com/scholar?q=Dong+YH%2C+Ding+JZ%2C+Jiang+X%2C+Li+G%2C+Li+Z%2C+Jin+Z%2E+CodeScore%3A+Evaluating+code+generation+by+learning+code+execution%2E+ACM+Trans%2E+on+Software+Engineering+and+Methodology%2C+2025%2C+34%283%29%3A+77%2E+%5Bdoi%3A+10%2E1145%2F36959)
146. Liu JW, Xia CS, Wang YY, Zhang LM. Is your code generated by ChatGPT really correct? Rigorous evaluation of large language models for code generation. In: Proc. of the 37th Int’l Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2023. 943. [find](https://scholar.google.com/scholar?q=Liu+JW%2C+Xia+CS%2C+Wang+YY%2C+Zhang+LM%2E+Is+your+code+generated+by+ChatGPT+really+correct%3F+Rigorous+evaluation+of+large+language+models+for+code+generation%2E+In%3A+Proc%2E+of+the+37th+Int%E2%80%99l+Conf%2E+on+Neural+Infor) [our card](https://md.research.elamur.ai/free/papers/liu-2023-your-code-generated-chatgpt-really/)
147. Hendrycks D, Basart S, Kadavath S, Mazeika M, Arora A, Guo E, Burns C, Puranik S, He H, Song D, Steinhardt J. Measuring coding challenge competence with APPS. arXiv:2105.09938, 2021. [find](https://scholar.google.com/scholar?q=Hendrycks+D%2C+Basart+S%2C+Kadavath+S%2C+Mazeika+M%2C+Arora+A%2C+Guo+E%2C+Burns+C%2C+Puranik+S%2C+He+H%2C+Song+D%2C+Steinhardt+J%2E+Measuring+coding+challenge+competence+with+APPS%2E+arXiv%3A2105%2E09938%2C+2021%2E) [our card](https://md.research.elamur.ai/free/papers/hendrycks-2021-measuring-coding-challenge-competence-apps/)
148. Jain N, Han K, Gu A, Li WD, Yan FJ, Zhang TJ, Wang SD, Solar-Lezama A, Sen K, Stoica I. LiveCodeBench: Holistic and contamination free evaluation of large language models for code. In: Proc. of the 13th Int’l Conf. on Learning Representations (ICLR). Singapore: OpenReview.net, 2025. 1–41. [find](https://scholar.google.com/scholar?q=Jain+N%2C+Han+K%2C+Gu+A%2C+Li+WD%2C+Yan+FJ%2C+Zhang+TJ%2C+Wang+SD%2C+Solar%2DLezama+A%2C+Sen+K%2C+Stoica+I%2E+LiveCodeBench%3A+Holistic+and+contamination+free+evaluation+of+large+language+models+for+code%2E+In%3A+Proc%2E+of+the+13) [our card](https://md.research.elamur.ai/free/papers/jain-2024-livecodebench-holistic-contamination-free-evaluation/)
149. Dong YH, Jiang X, Liu HY, Jin Z, Gu B, Yang MF, Li G. Generalization or memorization: Data contamination and trustworthy evaluation for large language models. In: Findings of the Association for Computational Linguistics: ACL 2024. Bangkok: ACL, 2024. 12039–12050. [doi: 10.18653/v1/2024.findings-acl… [find](https://scholar.google.com/scholar?q=Dong+YH%2C+Jiang+X%2C+Liu+HY%2C+Jin+Z%2C+Gu+B%2C+Yang+MF%2C+Li+G%2E+Generalization+or+memorization%3A+Data+contamination+and+trustworthy+evaluation+for+large+language+models%2E+In%3A+Findings+of+the+Association+for+Compu)
150. Jimenez CE, Yang J, Wettig A, Yao SY, Pei KX, Press O, Narasimhan KR. SWE-Bench: Can language models resolve real-world GitHub issues? In: Proc. of the 12th Int’l Conf. on Learning Representations (ICLR). Vienna: OpenReview.net, 2024. 1–51. [find](https://scholar.google.com/scholar?q=Jimenez+CE%2C+Yang+J%2C+Wettig+A%2C+Yao+SY%2C+Pei+KX%2C+Press+O%2C+Narasimhan+KR%2E+SWE%2DBench%3A+Can+language+models+resolve+real%2Dworld+GitHub+issues%3F+In%3A+Proc%2E+of+the+12th+Int%E2%80%99l+Conf%2E+on+Learning+Representations+%28IC) [our card](https://md.research.elamur.ai/free/papers/jimenez-2024-swe-bench-can-language-models/)
151. Xu K, Mao YW, Guan XY, Feng ZL. Web-Bench: A LLM code benchmark based on Web standards and frameworks. arXiv:2505.07473, 2025. [find](https://scholar.google.com/scholar?q=Xu+K%2C+Mao+YW%2C+Guan+XY%2C+Feng+ZL%2E+Web%2DBench%3A+A+LLM+code+benchmark+based+on+Web+standards+and+frameworks%2E+arXiv%3A2505%2E07473%2C+2025%2E)
152. Aider. Aider LLM benchmarks: Quantitative evaluation of AI coding models. 2024. [https://aider.chat/docs/benchmarks.html](https://aider.chat/docs/benchmarks.html) [↗](https://aider.chat/docs/benchmarks.html)
153. Li J, Li G, Zhang XM, Dong YH, Jin Z. EvoCodeBench: An evolving code generation benchmark aligned with real-world code repositories. arXiv:2404.00599, 2024. — [arXiv:2404.00599](https://arxiv.org/abs/2404.00599) [find](https://scholar.google.com/scholar?q=Li+J%2C+Li+G%2C+Zhang+XM%2C+Dong+YH%2C+Jin+Z%2E+EvoCodeBench%3A+An+evolving+code+generation+benchmark+aligned+with+real%2Dworld+code+repositories%2E+arXiv%3A2404%2E00599%2C+2024%2E+%E2%80%94+%5BarXiv%3A2404%2E00599%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F)
154. Li J, Li G, Zhao YF, Li YM, Liu HY, Zhu H, Wang LC, Liu KB, Fang Z, Wang LS, Ding JZ, Zhang XM, Zhu YQ, Dong YH, Jin Z, Li BH, Huang F, Li YB, Gu B, Yang MF. DevEval: A manually-annotated code generation benchmark aligned with real-world code repositories. In: Findings of the Association for Computa… [find](https://scholar.google.com/scholar?q=Li+J%2C+Li+G%2C+Zhao+YF%2C+Li+YM%2C+Liu+HY%2C+Zhu+H%2C+Wang+LC%2C+Liu+KB%2C+Fang+Z%2C+Wang+LS%2C+Ding+JZ%2C+Zhang+XM%2C+Zhu+YQ%2C+Dong+YH%2C+Jin+Z%2C+Li+BH%2C+Huang+F%2C+Li+YB%2C+Gu+B%2C+Yang+MF%2E+DevEval%3A+A+manually%2Dannotated+code+generat)
155. Hu JW, Zheng WC, Liu YH, Liu Y. Optimizing token consumption in LLMs: A nano surge approach for code reasoning efficiency. arXiv:2504.15989, 2025. — [arXiv:2504.15989](https://arxiv.org/abs/2504.15989) [find](https://scholar.google.com/scholar?q=Hu+JW%2C+Zheng+WC%2C+Liu+YH%2C+Liu+Y%2E+Optimizing+token+consumption+in+LLMs%3A+A+nano+surge+approach+for+code+reasoning+efficiency%2E+arXiv%3A2504%2E15989%2C+2025%2E+%E2%80%94+%5BarXiv%3A2504%2E15989%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2504%2E15989)
156. Zhao CG, Deng CQ, Ruan C, Dai DM, Gao HZ, Li JS, Zhang LY, Huang PP, Zhou SY, Ma SR, Liang WF, He Y, Wang YQ, Liu YX, Wei YX. Insights into DeepSeek-V3: Scaling challenges and reflections on hardware for AI architectures. In: Proc. of the 52nd Annual Int’l Symp. on Computer Architecture (ISCA). Toky… [find](https://scholar.google.com/scholar?q=Zhao+CG%2C+Deng+CQ%2C+Ruan+C%2C+Dai+DM%2C+Gao+HZ%2C+Li+JS%2C+Zhang+LY%2C+Huang+PP%2C+Zhou+SY%2C+Ma+SR%2C+Liang+WF%2C+He+Y%2C+Wang+YQ%2C+Liu+YX%2C+Wei+YX%2E+Insights+into+DeepSeek%2DV3%3A+Scaling+challenges+and+reflections+on+hardware+)
157. Zhang KC, Zhang HZ, Li G, You JL, Li J, Zhao YF, Jin Z. SEAlign: Alignment training for software engineering agent. arXiv: 2503.18455, 2025. — [arXiv:2503.18455](https://arxiv.org/abs/2503.18455) [find](https://scholar.google.com/scholar?q=Zhang+KC%2C+Zhang+HZ%2C+Li+G%2C+You+JL%2C+Li+J%2C+Zhao+YF%2C+Jin+Z%2E+SEAlign%3A+Alignment+training+for+software+engineering+agent%2E+arXiv%3A+2503%2E18455%2C+2025%2E+%E2%80%94+%5BarXiv%3A2503%2E18455%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2503%2E18455%29)
158. Jiang X, Dong YH, Liu MY, Deng HY, Wang T, Tao YD, Cao RY, Li BH, Jin Z, Jiao WP, Huang F, Li YB, Li G. CodeRL+: Improving code generation via reinforcement with execution semantics alignment. arXiv:2510.18471, 2025. [find](https://scholar.google.com/scholar?q=Jiang+X%2C+Dong+YH%2C+Liu+MY%2C+Deng+HY%2C+Wang+T%2C+Tao+YD%2C+Cao+RY%2C+Li+BH%2C+Jin+Z%2C+Jiao+WP%2C+Huang+F%2C+Li+YB%2C+Li+G%2E+CodeRL%2B%3A+Improving+code+generation+via+reinforcement+with+execution+semantics+alignment%2E+arXiv%3A2)
159. Lee H, Zhang ZQ, Lu HX, Zhang LM. SEC-bench: Automated benchmarking of LLM agents on real-world software security tasks. arXiv:2506.11791, 2025. — [arXiv:2506.11791](https://arxiv.org/abs/2506.11791) [find](https://scholar.google.com/scholar?q=Lee+H%2C+Zhang+ZQ%2C+Lu+HX%2C+Zhang+LM%2E+SEC%2Dbench%3A+Automated+benchmarking+of+LLM+agents+on+real%2Dworld+software+security+tasks%2E+arXiv%3A2506%2E11791%2C+2025%2E+%E2%80%94+%5BarXiv%3A2506%2E11791%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2506%2E11791%29)
160. Siddeeq S, Waseem M, Rasheed Z, Hasan MM, Rasku J, Saari M, Terho H, Makela K, Kemell KK, Abrahamsson P. LLM-based multi-agent system for intelligent refactoring of Haskell code. arXiv:2506.19481, 2025. — [arXiv:2506.19481](https://arxiv.org/abs/2506.19481) [find](https://scholar.google.com/scholar?q=Siddeeq+S%2C+Waseem+M%2C+Rasheed+Z%2C+Hasan+MM%2C+Rasku+J%2C+Saari+M%2C+Terho+H%2C+Makela+K%2C+Kemell+KK%2C+Abrahamsson+P%2E+LLM%2Dbased+multi%2Dagent+system+for+intelligent+refactoring+of+Haskell+code%2E+arXiv%3A2506%2E19481%2C+202)
161. Chen Z, Jiang LX. Evaluating software development agents: Patch patterns, code quality, and issue complexity in real-world GitHub scenarios. In: Proc. of the 2025 IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering (SANER). Montreal: IEEE, 2025. 657–668. [doi: 10.1109/SANER64311.2025.… — [doi:10.1109/saner64311.2025.00068](https://doi.org/10.1109/saner64311.2025.00068) [find](https://scholar.google.com/scholar?q=Chen+Z%2C+Jiang+LX%2E+Evaluating+software+development+agents%3A+Patch+patterns%2C+code+quality%2C+and+issue+complexity+in+real%2Dworld+GitHub+scenarios%2E+In%3A+Proc%2E+of+the+2025+IEEE+Int%E2%80%99l+Conf%2E+on+Software+Analysis)
162. GitHub. GitHub Copilot: Your AI pair programmer. 2021. [https://github.com/copilot](https://github.com/copilot) [↗](https://github.com/copilot)
163. Cognition AI. Devin: The first AI software engineer. 2024. [https://devin.ai](https://devin.ai) [↗](https://devin.ai)
164. Anysphere, Inc. Cursor: The AI-first code editor. 2023. [https://www.cursor.com](https://www.cursor.com) [↗](https://www.cursor.com)
165. Alibaba Cloud. Tongyi Lingma: Intelligent coding assistant tool based on Tongyi large model. 2023 (in Chinese). [https://lingma.aliyun.com](https://lingma.aliyun.com) [↗](https://lingma.aliyun.com)
166. Anthropic. Claude Code: A command-line tool for agentic coding. 2024. [https://www.anthropic.com/claude-code](https://www.anthropic.com/claude-code) [↗](https://www.anthropic.com/claude-code)
167. Hu EJ, Shen YL, Wallis P, Allen-Zhu Z, Li YZ, Wang SA, Wang L, Chen WZ. LoRA: Low-rank adaptation of large language models. In: Proc. of the 10th Int’l Conf. on Learning Representations (ICLR). OpenReview.net, 2022. 1–13. [find](https://scholar.google.com/scholar?q=Hu+EJ%2C+Shen+YL%2C+Wallis+P%2C+Allen%2DZhu+Z%2C+Li+YZ%2C+Wang+SA%2C+Wang+L%2C+Chen+WZ%2E+LoRA%3A+Low%2Drank+adaptation+of+large+language+models%2E+In%3A+Proc%2E+of+the+10th+Int%E2%80%99l+Conf%2E+on+Learning+Representations+%28ICLR%29%2E+OpenRe)
168. Hua WY, Yang XJ, Jin MY, Li ZL, Cheng W, Tang RX, Zhang YF. TrustAgent: Towards safe and trustworthy LLM-based agents. In: Findings of the Association for Computational Linguistics: EMNLP 2024. Miami: ACL, 2024. 10000–10016. [doi: 10.18653/v1/2024. findings-emnlp.585] [find](https://scholar.google.com/scholar?q=Hua+WY%2C+Yang+XJ%2C+Jin+MY%2C+Li+ZL%2C+Cheng+W%2C+Tang+RX%2C+Zhang+YF%2E+TrustAgent%3A+Towards+safe+and+trustworthy+LLM%2Dbased+agents%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2024%2E+Miami%3A)
169. Zhang RS, Javidnia N, Sheybani N, Koushanfar F. Robust and secure code watermarking for large language models via ML/Crypto codesign. arXiv:2502.02068, 2025. 附中文参考文献 [165] 阿里云. 通义灵码 (Tongyi Lingma): 基于通义大模型的智能编码辅助工具. 2023. [https://lingma.aliyun.com](https://lingma.aliyun.com) 作者简介 董益宏, 博士生, CCF 学生会员, 主要研究领域为智能化软件工程, 多智能体, 大语言模… — [arXiv:2502.02068](https://arxiv.org/abs/2502.02068) [↗](https://lingma.aliyun.com)