Introduction
Code Large Language Models (CodeLLMs) have shown proficiency in generating code but struggle with complex software engineering tasks. Recent works introduced multi-agent frameworks for software development, aiming to mimic real-world software development.
Introducing AgileCoder
FPT Software AI Center researchers propose AgileCoder, a novel framework inspired by Agile Methodology, widely used in professional software development. AgileCoder’s structure is based on Agile’s concept of software evolution and structured development in sprints.
Agent Roles and Collaboration
AgileCoder involves agents such as Project Manager, Scrum Master, Developer, Senior Developer, and Tester, collaborating across sprints following Agile methodology. It emphasizes dynamic adaptability and iterative development, refining outputs and problems from previous sprints in subsequent ones.
Dynamic Code Graph Generator
A key innovation in AgileCoder, the Dynamic Code Graph Generator, creates a Code Dependency Graph (CDG) to model code relationships and updates with source code changes. It plays a crucial role in rational testing plans and efficient code retrieval.
Evaluation and Results
Evaluations demonstrate AgileCoder’s superior performance on benchmarks like HumanEval, MBPP, and ProjectDev. It outperforms existing methods in generating software from complex requirements.
Conclusion
AgileCoder is a novel multi-agent software development framework inspired by Agile methodology, emphasizing dynamic adaptability and iterative development. It showcases superiority over existing methods, making it a promising approach for complex software development tasks.
Check out the Paper and GitHub. All credit for this research goes to the researchers of this project.
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