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

Understand

The advancement of natural language processing (NLP) has been significantly boosted by the development of transformer-based large language models (LLMs).

• These models have revolutionized NLP tasks, particularly in code generation, aiding developers in creating software with enhanced efficiency.
• Despite their advancements, challenges in balancing code snippet generation with effective test case generation and execution persist.
• To address these issues, this paper introduces Multi-Agent Assistant Code Generation (AgentCoder), a novel solution comprising a multi-agent framework with specialized agents: the programmer agent, the test designer agent, and the test executor agent.