Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent System
Leilei Lin, Yingming Zhou, Wenlong Chen, Chen Qian

TL;DR
This paper introduces ToP, a dynamic process generation framework that uses experiential knowledge and process mining to enable LLM-guided multi-agent software development adaptable to real-time changes.
Contribution
The paper presents a novel framework that dynamically generates and refines software development processes using LLMs, process mining, and heuristic filtering, improving flexibility over fixed process models.
Findings
Enhances GPT-3.5 and GPT-4's process generation capabilities.
Significantly improves adaptability for five software development task categories.
Uses process mining to derive and refine process models.
Abstract
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…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Collaboration in agile enterprises · Multi-Agent Systems and Negotiation
