流程上的思考:多代理系统协作开发的动态流程生成

Leilei Lin, Yingming Zhou, Wenlong Chen, Chen Qian
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引用次数: 0

摘要

软件开发是一项协作性工作,需要来自不同部门的人员通力合作,共同开发出高质量的软件系统。在这种情况下,人们开始探索利用基于 LLM 的多代理系统进行软件开发的方法。然而,现有的研究往往将软件开发过程僵化地固定在代码形式的框架中,无法实时动态地调整软件开发过程,以适应更加灵活多变的软件环境。在本文中,我们提出了一种动态流程生成框架,命名为 ToP(Think-on-Process)。ToP 的核心思想是利用经验知识(即流程模型)指导 LLM 生成软件开发流程(即实例)。这些实例将指导多机器人进行软件开发,并利用编译器提供开发结果反馈。随后,我们利用启发式算法对实例进行过滤,并应用流程挖掘算法生成流程模型。最后,流程模型将被转换成文本,格式为提示,以增强 LLM 生成其他实例的能力。实验证明,我们的 ToP 框架大大提高了 GPT-3.5 和 GPT-4 针对五类软件开发任务的动态流程生成能力。
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Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent System
Software development is a collaborative endeavor that requires individuals from different departments to work together in order to collectively develop a high-quality software system. In this context, people have begun to explore a method that leverages multi-agent systems based on LLMs to carry out software development. However, existing research tends to rigidly fix the software development process in a framework in code form, thus failing to dynamically adjust the software development process in real-time to meet the more flexible and variable software environment. In this paper, we propose a dynamic process generation framework, named ToP (Think-on-Process). The core idea of ToP is to leverage experiential knowledge (i.e., process models) to guide LLMs in generating software development processes (i.e., instances). These instances will guide multi-agent in software development and employ a compiler to provide feedback on the development outcomes. Subsequently, we utilize heuristic algorithms to filter the instances and apply process mining algorithms to derive process model. Finally, the process model will be converted into text, formatted as prompts, to enhance the ability of LLMs to generate other instances. Experiments demonstrate that our framework ToP significantly enhances the dynamic process generation capability of the GPT-3.5 and GPT-4 for five categories of software development tasks.
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