AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

By Dong Huang, Qingwen Bu, Jie M. Zhang, Michael Luck, Heming Cui. arXiv.org, 2023

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/1db56be01aeb44ca0f3fcebb45180cab1e4cd82e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nitesh V. Chawla
2. Francisco Herrera
3. Gianluca Bontempi
4. Andrea Dal Pozzolo
5. Charu C. Aggarwal
6. Alberto Fernández
7. Vasile Palade
8. Taghi M. Khoshgoftaar

In Information Systems

1. Nitesh V. Chawla
2. Gianluca Bontempi
3. Andrea Dal Pozzolo
4. Sulaiman M.N
5. H. M.
6. Ismail Mohamad
7. José Antonio Vilar
8. Pablo Montero

Citations in the corpus

• Large Language Model based Multi-Agents: A Survey of Progress and Challenges (2024, background): “[ Huang et al. , 2023a ] delves deeper into multi-agent based programming by solving the problem of balancing code snippet generation with effective test case generation, execution, and optimization.”
• A Survey on Large Language Models for Code Generation (2024, neutral): “Finally, for the model’s good and reliable performance, the paper found the following critical components: a high-quality competitive programming dataset, efficient transformer models, and large-scale sampling.”