The DOI matches its Crossref record; 40 of the 54 references it cites were found in a registry or an index.
ChatDev: Communicative Agents for Software Development
Open accessAbstract
Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, Juyuan Xu, Dahai Li, Zhiyuan Liu, Maosong Sun. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In ChatDev [153], for example, a virtual CEO meta-agent distributes subtasks to departmental agents and integrates their outputs into a unified strategic response.”
From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [153]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Multiple LLM agents working in parallel can solve tasks that are too large for any single agent to finish within its iteration budget (Dong et al., 2024; Qian et al., 2024; Hong et al., 2024; Geng and Neubig, 2026; Qu et al., 2026).”
From Multi-agent Collaboration with State Management (2026) · cites this paper as Qian et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“yet most of them still rely on natural-language soft constraints rather than mathematically explicit safety boundaries.”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Such soft coordination can be brittle under adversarial prompts, hallucinated critiques, or multi-turn confirmation bias.”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Such frameworks are particularly well-suited for multi-agent execution scenarios with well-defined agent execution sequences, as exemplified by programming tasks [50, 85] and conversational games [111], as they can significantly simplify complex agent interaction processes.”
From AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society (2025) · cites this paper as [85]
The cited source says what the claim attributes to it.
In the citing paper
“This formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software (Qian et al., 2024; Wang et al., 2025l) or conducting scientific inquiry (Weng et al., 2025).”
From Memory in the Age of AI Agents (2025) · cites this paper as Qian et al., 2024
The cited source says what the claim attributes to it.
In the citing paper
“This formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software (Qian et al., 2024; Wang et al., 2025l) or conducting scientific inquiry (Weng et al., 2025).”
From Memory in the Age of AI Agents (2025) · cites this paper as Qian et al., 2024
The cited source says what the claim attributes to it.
In the citing paper
“Building a software system from scratch requires agents not only to generate code snippets, but also to translate high-level requirements into executable plans, coordinate interdependent tasks, design and integrate components, and continuously test and debug the evolving system [8, 32, 36].”
From Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement (2026) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Multi-agent coding systems [Qian et al., 2024, Hong et al., 2024, Nguyen et al., 2024, Huang et al., 2024, Ishibashi and Nishimura, 2024, Pan et al., 2026, Islam et al., 2025, Peng et al., 2026, Zhu et al., 2025, Wu et al., 2023, Wang et al., 2025, Park et al., 2023, Chen et al., 2023, Shinn et al., 2023, Ashrafi et al., 2025, Grötschla et al., 2025, Shafin et al., 2025] predominantly use sequ”
From AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace (2026) · cites this paper as Qian et al., 2024
The cited source says what the claim attributes to it.
In the citing paper
“Multi-agent coding systems improve decomposition and repair by assigning roles to planners, developers, testers, and reviewers [11,12,13]”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
The cited source says what the claim attributes to it.
In the citing paper
“When subtasks exhibit strict contextual dependencies, hierarchical (Zhang et al., 2024c; Qian et al., 2024) workflows offer a structured alternative.”
From A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems (2025) · cites this paper as Qian et al., 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 13
- Thorsten Brants, Ashok C Popat, Peng Xu, Franz J Och, and Jeffrey Dean. 2007. Large Language Models in Machine Translation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 858- 867.
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Ad- vances in Neural Information Processing Systems (NeurIPS), volume 33, pages 1877-1901.
- Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. 2023b. AgentVerse: Facilitating Multi-agent Collaboration and Explor- ing Emergent Behaviors in Agents. In International Conference on Learning Representations (ICLR).
- Robert France and Bernhard Rumpe. 2007. Model- driven Development of Complex Software: A Re- search Roadmap. In Future of Software Engineering (FOSE), pages 37-54.
- Peter Freeman, Donald J. Bagert, Hossein Saiedian, Mary Shaw, Robert Dupuis, and J. Barrie Thompson. 2001. Software Engineering Body of Knowledge (SWEBOK). In Proceedings of the International Conference on Software Engineering (ICSE), pages 693-696.
- Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2023. MetaGPT: Meta Pro- gramming for A Multi-Agent Collaborative Frame- work. In International Conference on Learning Rep- resentations (ICLR).
- Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023a. CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS).
- Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023. CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthe- sis. In The International Conference on Learning Representations (ICLR).
- Florian Pudlitz, Florian Brokhausen, and Andreas Vo- gelsang. 2019. Extraction of System States from Natural Language Requirements. In IEEE Interna- tional Requirements Engineering Conference (RE), pages 211-222. · DOI
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Advances in Neural Information Pro- cessing Systems (NeurIPS), volume 30.
- Chengcheng Wan, Shicheng Liu, Sophie Xie, Yifan Liu, Henry Hoffmann, Michael Maire, and Shan Lu. 2022. Automated Testing of Software that Uses Ma- chine Learning APIs. In IEEE/ACM International Conference on Software Engineering (ICSE), pages 212-224. · DOI
- Song Wang, Nishtha Shrestha, Abarna Kucheri Subbu- raman, Junjie Wang, Moshi Wei, and Nachiappan Nagappan. 2021. Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We? In IEEE/ACM International Conference on Software Engineering (ICSE), pages 1548-1560. · DOI
- Jonas Winkler, Jannis Grönberg, and Andreas Vogel- sang. 2020. Predicting How to Test Requirements: An Automated Approach. In Software Engineering, volume P-300 of LNI, pages 141-142. · DOI
Cited by
Sorted by citations
Items 201–300 of 497
LLM-Based Multiagent System for Construction Job Safety Analysis
Ghorab, K., & Zangeneh, P. (2026) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-7335 · Cited by 1
Wang, L., Sun, S., Wang, X., Gu, J., & Liu, K. (2026) · Target International Journal of Translation Studies · doi:10.1075/target.25081.wan · Cited by 2
Mou, Z., Tian, Z., Jin, J., Yu, H., & Huang, Y. (2026) · Biomolecules · doi:10.3390/biom16060830 · Cited by 1
Wink: Recovering from Misbehaviors in Coding Agents
Nanda, R., Maddila, C., Jha, S., Khan, E. M., Paltenghi, M., & Chandra, S. (2026) · ACM International Conference on AI-Powered Software (AIware) · doi:10.1145/3805760.3814911 · Cited by 23
Yuvaraj, N., Kumar, M. S., & Manoj, M. S. (2026) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn70047.2026.11594381 · Cited by 1
Olariu, F., & Alboaie, L. (2026) · IEEE Access · doi:10.1109/access.2026.3712587 · Cited by 1
Zhang, X., & Yang, B. (2026) · IEEE Access · doi:10.1109/access.2026.3722027 · Cited by 1
Intelligent Agents for Smart Agriculture: Architectures, Applications, and Future Challenges
Tao, W., Sang, Q., Chen, C., & Mao, Q. (2026) · Agriculture · doi:10.3390/agriculture16171808 · Cited by 2
Mitrović, S., Giuffrida, V., Rosselli, M., Bosetti, M., & Salani, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39395-1_23 · Cited by 1
Don't Let AI Agents YOLO Your Files: Information and Control in Agent-Native Filesystems
Zhong, S., Liao, J., Liu, J., Zheng, M., Arpaci-Dusseau, A. C., & Arpaci-Dusseau, R. H. (2026) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3830418.3843858 · Cited by 9
Wang, L., Li, Q., Cui, D., Wang, M., Zhao, Y., Xu, Y., Zhuang, H., Zhou, Y., & Wang, L. (2025) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc66645.2025.00067 · Cited by 1
Bargigli, L., Iannucci, G., & Ricchiuti, G. (2025) · Journal of Economic Interaction and Coordination · doi:10.1007/s11403-025-00445-3 · Cited by 1
Orchestration of No-Code Agents in the AgentFlow FrameworkPreprint
Lande, D., & Strashnoy, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5381820 · Cited by 1
Smoothing Grounding and Reasoning for MLLM-Powered GUI Agents with Query-Oriented Pivot TaskPreprint
Wu, Z., Cheng, P., Wu, Z., Ju, T., Zhang, Z., & Liu, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5770750 · Cited by 1
BenjiTest: An Agentic Framework for Evaluating LLM-Based Unit Test Generation and CorrectionPreprint
Lops, A., Ragone, A., Penta, M. D., Trizio, M., & Narducci, F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6843084 · Cited by 1
Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity
Yang, Y., Qu, C., Wen, M., Shi, L., Wen, Y., Zhang, W., Wierman, A., & Gu, S. (2026) · arXiv · doi:10.48550/arxiv.2602.03794 · Cited by 1
Gómez Álvarez, S., Mozo Quesada, A., Navarro, T., Gálvez Rojas, S., & López Valverde, F. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01086-z · Cited by 1
Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement
Yan, H., Su, M.-L., Zhang, H., Li, Z., Zhang, C., Zhang, S., Chen, Y., Bai, L., & Hu, S. (2026) · arXiv · doi:10.48550/arxiv.2609.01481 · Cited by 5
Wang, W., Piękos, P., Nanbo, L., Laakom, F., Chen, Y., Ostaszewski, M., Zhuge, M., & Schmidhuber, J. (2025) · arXiv · doi:10.48550/arxiv.2510.21614 · Cited by 40
“ChatDev (Qian et al., 2023) first illustrated this idea in the context of automated bug fixing, and similar frameworks were later explored in SWE Yang et al. (2024), OpenHands (Wang et al., 2024), MetaGPT (Hong et al., 2024), and AgentLess (Xia et al., 2025).”
TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing
Bian, Z., Wu, F., Zhang, C., Dong, H., Liang, Y., & Zhuo, Y. (2026) · arXiv · doi:10.48550/arxiv.2604.03143
SE-Blackboard: A Shared-State Architecture for Multi-Agent Software Engineering Pipelines
Liu, E., Zhu, Q., & Dong, X. (2026) · IEEE Access · doi:10.1109/access.2026.3695435
“Systems such as MetaGPT [2], ChatDev [3], SWE-Agent [4], and AgentCoder [5] demonstrate that multiple AI agents can collaboratively solve complex tasks, including code generation [6], bug fixing, and code review, representing a shift from monolithic tools toward modular, role-based architectures.”
Effective Strategies for Asynchronous Software Engineering Agents
Geng, J., & Neubig, G. (2026) · arXiv · doi:10.48550/arxiv.2603.21489 · Cited by 23
“Early frameworks such as CAMEL [Li et al., 2023] and Generative Agents [Park et al., 2023] established the foundation for communicative interaction, which was later structured into a natural language communication pipeline from ChatDev [Qian et al., 2024a].”
Multi-agent Collaboration with State Management
Liu, M., Chen, T., Xu, Z., Jiang, X., & Dong, Y. (2026) · arXiv
“Subsequently, ChatDev and MetaGPT model software development as a structured workflow among specialized agents (Qian et al., 2024; Hong et al., 2024).”
Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers
Du, P. (2026) · arXiv · Cited by 1
“Coding agents assist with generation, debugging, review, and project management across codebases that may contain millions of lines [Qian et al., 2024, Hong et al., 2024].”
ReproAgent: Contract-Guided Paper-to-Code Reproduction
Hu, X., Pan, Z., Wang, Z., Liu, Z., Su, Z., & Zhang, W. (2026) · arXiv · doi:10.48550/arxiv.2608.24291
“…(Baek et al., 2024; Lu et al., 2024; Schmidgall et al., 2025), ML engineering (Chan et al., 2024; Tang et al., 2024), and code-generation or repository-level coding settings (Qian et al., 2024; Islam et al., 2024; Hong et al., 2024; Yang et al., 2024; Xia et al., 2025; Zhang et al., 2024).”
Self-citationMAPLE-Guard: Memory-Aware Link Enforcement Against Memory-Link Poisoning in Multi-Agent Systems
Xiong, W., Zhou, Y., Wang, J., Gu, S., Tang, B., Li, Z., Xiong, F., Wen, Y., & Wen, M. (2026) · arXiv · doi:10.48550/arxiv.2608.00426 · Cited by 2
“LLM-based MAS coordinate agents through roles, workflows, and communication [15, 22, 34, 49].”
CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation
Pugachev, S. (2025) · arXiv · doi:10.48550/arxiv.2510.18893 · Cited by 13
“Multi-agent LLM systems promise parallel speedups but existing approaches fail: sequential frameworks (Chat-Dev (Chen et al., 2024), MetaGPT (Hong et al., 2023)) employ waterfall/pipeline workflows precluding concurrent execution; lock-based coordination introduces O(N × L) contention (N agents…”
Ning, X., Tieu, K., Fu, D., Wei, T., Li, Z., Bei, Y., Zou, J., Ai, M., Liu, Z., Li, T.-W., et al. (2026) · arXiv · doi:10.48550/arxiv.2605.18747
Self-citationLi, Z., Li, Z., Guo, Z., Ren, X., & Huang, C. (2025) · arXiv · doi:10.48550/arxiv.2512.07921
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Huang, W.-C., Zhang, W., Liang, Y., Bei, Y., Chen, Y., Feng, T., Pan, X., Tan, Z., Wang, Y., Wei, T., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.06052 · Cited by 70
Self-citationAgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace
Cho, S., & Lee, D. (2026) · arXiv · doi:10.48550/arxiv.2608.23740 · Cited by 2
When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
Destefanis, G., & Aste, T. (2026) · arXiv · doi:10.48550/arxiv.2608.16801 · Cited by 3
Decentralized Multi-Agent Systems with Shared Context
Mao, Y., Gu, J., Chauhan, A., Zhang, Q., Kang, H., & Mirhoseini, A. (2026) · arXiv · doi:10.48550/arxiv.2606.10662
“MetaGPT (Hong et al., 2023) and ChatDev (Qian et al., 2024) further structure collaboration through software-engineering-inspired roles, communication protocols, and standardized workflows, demonstrating that specialization can reduce coordination complexity in multi-step tasks.”
On the Potential of Large Language Models in Parallel Systems: A Systematic Analysis
Cheng, X., Luo, S., Yang, J., Lei, Y., Li, Z., & Yang, L. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10864500
A Method for Detecting Knowledge Conflicts in Chinese Intelligent Agent Interactions
Cheng, H., Gu, Y., Xi, M., Zhong, Q., & Liu, W. (2025) · Interdisciplinary Journal of Information Knowledge and Management · doi:10.28945/5497
Self-citationHigh Order Automatic Design of Agentic Systems
Santosh, K., Shukla, S., Mohanty, S., Acharya, A. A., & Panda, N. (2025) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic64892.2025.11158363
Research on Personalized Postgraduate Training Mode Based on AI Agent and DeepSeek
Zheng, D., Chen, L., & Zhang, X. (2025) · 2025 5th International Conference on Artificial Intelligence and Education (ICAIE) · doi:10.1109/icaie64856.2025.11158643
AutoFlowgen: A Multi-Agent Framework for Automated Generation of Workflows from Natural Language
Yang, Z., Haihong, E., Tang, Z., Liu, J., Liu, Y., & Zheng, Q. (2025) · International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) · doi:10.1109/caibda65784.2025.11182955
Xiao, H., Sun, J., Yu, S., Qin, L., Luo, H., & Li, Y. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07957-6
AI Agents: From Concept to Code to Commerce
Ishida, T., Murakami, Y., Lin, D., & Lhaksmana, K. M. (2025) · International Conference on Information and Communicatiaon Technology · doi:10.1109/icoict66265.2025.11193130 · Cited by 2
Yang, Z., Zhao, M., & Chiu, H.-H. (2024) · International Symposium of Chinese CHI (Chinese CHI) · doi:10.1145/3758871.3758931
SCGen-Agent: Automated Generation of Smart Contract Based on Multi-agent Collaboration
Ji, Y., Li, Y., Li, R., Hao, X., Yu, D., & Chen, Y. (2025) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc66434.2025.00017
Towards Trustworthy Legal AI: A Multi-Agent Approach to Integrating Legislative Knowledge
Matak, M., & Chudziak, J. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09318-9_1
Enhancing Edge Microservice Deployment Efficiency with an LLM-Empowered Multi-agent Framework
Gao, S., Wang, Z., Zhao, Y., & Li, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4088-4_26 · Cited by 1
AUTOTEST: Automated MCQ Generation for Exam Preparation Using Agentic RAG
Shivani, S., Prasad, S., Kumar, G. S., Satyam, K., & Sampatrao, G. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8750-3_12
Multi-Level Protocol Consistency Verification Using Large Language Model Agents
Zhang, W., Chen, H., Wang, Y., & Qiang, W. (2025) · International Symposium on Computer and Information Processing Technology (ISCIPT) · doi:10.1109/iscipt67144.2025.11265514
RAG Multiagente para Recuperação de Conhecimento Organizacional
Moretti, G. L., Sousa, K. D. S., Mendes, M. D. S., & de Oliveira, M. A. (2025) · Anais do Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC) · doi:10.5753/wesaac.2025.37548
Software Development Using a Multi-agent Approach
Ribeiro, J. G., Pires, J. M., Pereira, J. O., Dur�ão, F. A., & Ralha, C. G. (2025) · Anais do Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC) · doi:10.5753/wesaac.2025.37549
MCPybarra: A Multi-agent Framework for Low-Cost, High-Quality MCP Service Generation
Peng, B., Liu, M., Liu, Y., Tian, C., Yu, S., & Wang, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5012-8_4
Uchoa, A. P., Oliveira, C., Motta, C. L. R., & Schneider, D. (2026) · Computers · doi:10.3390/computers15010024 · Cited by 2
Test Case Enhanced Self-Iterative Code Generation Framework
Chang, T., Zhang, Y., & Fang, Y.-J. (2025) · International Conference on Parallel and Distributed Systems · doi:10.1109/icpads67057.2025.11322891
An Agent-based Evaluation Framework for Complex Code Generation
Wang, X., Hu, R., Gao, P., Peng, C., & Gao, C. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00200 · Cited by 11
Qiu, Q., Zhang, Y., Liu, X., Yi, L., Hu, W., & Zhuge, Q. (2025) · Asia Communications and Photonics Conference · doi:10.1109/acp66871.2025.11350980
OmniNova: A General Multimodal Multi-Agent Framework
Li, B., Wang, Z., Du, P., & Jiang, Y. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00393
A Systematic Literature Review on Intelligent Tools and the Developer Experience
Galván, D. A. Á., Benítez–Guerrero, E., & Mezura-Godoy, C. (2025) · International Conference in Software Engineering Research and Innovation (CONISOFT) · doi:10.1109/conisoft66928.2025.00046
Adithya, V., S, R. K. J., & Babu, N. V. (2025) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca66444.2025.11383462
Li, D., & Zhu, W. (2025) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice68195.2025.11382423 · Cited by 1
Self-citationLeveraging RAG and AI Agents to Enhance Specialized LLMs in Education
Jiani, L. E., Filali, S. E., Tazi, M., & Moujar, A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15147-6_6
AgentTCP: A Collaborative Multi-Agent Framework for Change-Aware Test Case Prioritization
Chen, J., Li, X., Wang, J., Xie, H., Liu, C., Wu, Z., & Lei, Y. (2025) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec66846.2025.00066
Collaborative Multi-Agent Framework for Autonomous Code Generation and Debugging
Nazareth, P., Uppoor, P., N, S. H., R, S. S., & Poojary, Y. (2025) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict67193.2025.11399205
Reliable Code Generation with Test Case Prioritization and Cognitive Validation
Huang, L., Yin, X., Li, X.-R., & Ni, C. (2025) · Asia-Pacific Software Engineering Conference · doi:10.1109/apsec66846.2025.00021
Jiang, G., Li, J., Ma, X., & Luo, Q. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789094
Self-citationJiang, Z., Xu, W., & Dong, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6353019
Policy-Governed Agentic Autonomous Quality Assurance for Enterprise Systems
Kunda, A. K. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4933 · Cited by 1
Optimizing Agentic Code Generation: Cost Efficiency, Observability and Orchestration
Kaplunovich, A. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402643
MATCH: Multi-Agentic Evidence Grounding for Explainable Hate Video Detection
Li, K., Hong, R., Lang, J., Wu, J., Zhou, F., & Song, J. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3672052 · Cited by 2
FTDI: A Budget-Aware Self-Healing Framework for Resilient LLM Multi-Agent Code GenerationPreprint
Men, S., Tong, Q., Zuo, R., & Lu, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6387100
WMAS: A Multi-Agent System Towards Intelligent and Customized Wireless Networks
Peng, J., Yuan, D., Ren, B., Fan, J., Wu, Z., & Yang, L. (2025) · Globecom · doi:10.1109/globecom59602.2025.11432052
SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation
Li, J., Wu, T., Mao, Y., Gao, Y., Feng, Y., & Liu, H. G. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3778092.3778093 · Cited by 10
Self-citationSun, S., & Fu, Y. (2026) · Buildings · doi:10.3390/buildings16061265
Aligning Agents With Experts in Material Selection: Prompting, Size, and Reasoning
Ying, M. Y., Grandi, D., Groom, A., & McComb, C. (2026) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4071467
An Agentic AI Framework for Strengthening Preventive Healthcare
Arul Kumaran, G., Balasastha, E., Jayakumar, N., & Jayakumar, R. (2026) · International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) · doi:10.1109/iciscois62701.2026.11447737
TheBotCompany: Self-Organizing Multi-agent Systems for Continuous Software Development
Lyu, W., Xiao, Y., Zhang, Y., & Sun, Y. (2026) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3832783.3834358 · Cited by 3
Chintalapati, J., Akhil Rajeev, P., Choudhury, B., Bedare, M., & Kumar, Y. (2025) · Journal of the Indian Institute of Science · doi:10.1007/s41745-026-00501-2
From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents
Chang, W.-Y., Huang, T., Chen, C.-D., & Chen, Y.-N. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434776
Wang, Z., & Fu, J. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_8
From Symbols to Synapses: The Reemergence of Agency in the Large Language Model Era
An, B. (2026) · IEEE Intelligent Systems · doi:10.1109/mis.2026.3668397
Fat-Cat: Document-Driven Metacognitive Multi-Agent System for Complex ReasoningPreprint
Yang, T., Wu, A., Wang, Y., Kang, J., Li, Y., Ren, J., Xin, Y., & Yuan, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6555840
Qiu, Z., Liu, J., Xia, Y., Shan, W., Guo, J., Liu, P., & Qi, H. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104642
Zhang, Z., Yi, X., Dai, Z., Yu, X., Zhang, S., Liu, B., Cao, J., & Zhao, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791926 · Cited by 2
AgentComm: Semantic Communication for Embodied Agents
Jiang, P., Feng, Y., Guo, J., Wen, C.-K., & Jin, S. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3729940
LLM Agent-Based Simulations for Phishing Email Detection in Various Multi-Agent Sizes
Teaño, A. R., & Victorino, J. N. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11467835
Ede-Slm: Execution-Driven Ensembles for Sub-1B Code Generation Models
Dondeti, R. (2026) · IEEE SoutheastCon · doi:10.1109/southeastcon63549.2026.11476694
TDPGen: Optimizing Agentic Code Generation via Test-Driven Planning and Hierarchical ReAct
Li, M., Yuan, S., & Han, M. (2026) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1109/iscait69154.2026.11477183
Boosting Knowledge Sharing Among Agents via Graph Decoupling
Zhang, H., Hu, H., Cheng, G., Gan, L., Wei, H., & Huang, J. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462380
Lin, Z., Shen, Y., Cai, Q., Sun, H., Zhou, J., & Xiao, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462205 · Cited by 3
DIAL: Database-Informed Interactive Multi-Agent System Loop for Personalized Image Generation
Sato, T., Maeda, K., Saito, N., Ogawa, T., & Haseyama, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463391
LLM-based multi-agent systems for code generation: A multi-vocal literature review
Rasheeda, Z., Waseema, M., Kemella, K.-K., Saari, M., & Abrahamsson, P. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.113142 · Cited by 3
Unicoon: Hypergraph-based Multi-Agent Simulation of Information Cocoons
Wei, C., Tu, Y., & Wang, Y. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792486
A Survey on LLM Agents: Architecture, Applications, and Challenges
Lee, D., & Kim, H. (2026) · International Conference on Information Networking · doi:10.1109/icoin68469.2026.11480623 · Cited by 2
STARK: A Comprehensive Framework for PLC Structured Text Assistant for Reasoning and Knowledge
Saha, D., Chatterjee, B., Gain, S., Sinha, M., Dasgupta, T., & Bhaumik, C. (2026) · Innovations in Software Engineering Conference (ISEC) · doi:10.1145/3796563.3796575
CC-RAG: A Collaborative-Critical Dual-Agent Framework for Enhanced Retrieval-Augmented Generation
Fang, Y., Cheng, Y., & Zhang, W. (2025) · International Conference on Intelligent Education and Intelligent Research (IEIR) · doi:10.1109/ieir67391.2025.11491388
HOLA: Hierarchical OODA-LLM Agent Architecture With Commander-Like Cognition
Kuang, X., Zeng, D., Li, G., Yang, F., & Li, X. (2026) · IEEE Transactions on Games · doi:10.1109/tg.2026.3689572 · Cited by 1
An LLM-Based Approach for Automatic ML Prototype Review
Coban, S., Perez, M., Akpinar, C., Tanriverdi, B., & Lichter, H. (2026) · IEEE International Conference on Software Analysis, Evolution and Reengineering - Companion (SANER-C) · doi:10.1109/saner-c67878.2026.00044
Siddiqi, A. M. U., & Varshney, M. (2026) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v7.i9s.2026.7999
Wang, X., Sharma, D. K., Kumar, D., & Tang, X. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3815586
Budgeted agentic AI for adaptive lightweight model fine-tuning
Wang, Y.-C., Sun, Q.-Y., & Xu, J. (2026) · Multiagent and Grid Systems · doi:10.1177/15741702261450097
Self-citationMulti-Agent LLM Framework for Imageability Assessment in Trusted Multimodal Data Spaces
Zhang, B., Huang, G., & Xu, C. (2025) · International Conference on Big Data Economy and Information Management (BDEIM) · doi:10.1145/3800000.3800222
Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective
Wadlom, N., Shen, J., & Lu, Y. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3802046
Agent Evolution: Capability-Mechanism Co-evolution
Chen, Y.-F., Zhang, C., Zheng, X., & Tang, S. (2026) · 2026 International Conference on Generative Artificial Intelligence and Information Security (GAIIS) · doi:10.1109/gaiis69281.2026.11519194
Metadata
- Authors
- Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., & Sun, M.
- Affiliations
- The University of Sydney (AU)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 15174-15186
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402684050
- Semantic Scholar
- 9474b463654fd92a62c103b21f95aed94b087907
- Cited by
- 490
- References
- 0
- Citations by year
- 2026: 313 · 2025: 151 · 2024: 26
- FWCI
- 157.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Tencent
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, software development, code generation, code review, developer communication, chatbots
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
