UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Temitayo Olamilekan Ogunsusi, Lijun Qian, Xishuang Dong
• Venue: arXiv: cs.AI
• Published: 2026-07-02
• Collected: 2026-07-03
• URL: http://arxiv.org/abs/2607.02186v1
• PDF: https://arxiv.org/pdf/2607.02186v1
• DOI: UNKNOWN

Plain-Language Brief

The research paper proposes UA-ChatDev, a multi-agent software development framework that is designed to mitigate issues caused by the propagation of incorrect decisions during software development. This is achieved by integrating uncertainty quantification into agent interactions and introducing a mechanism for selectively triggering verification when necessary.

What the Document Says

• Software development involves multiple agents with different roles.
• Existing autonomous multi-agent software development frameworks use role-based collaboration to automate various phases like requirements analysis, coding, testing, and refinement.
• However, these frameworks assume that all intermediate agent outputs are equally reliable, leading to a problem known as hallucination propagation where incorrect decisions can affect final software quality.
• The paper introduces UA-ChatDev, an uncertainty-aware multi-agent software development framework that addresses this challenge by incorporating uncertainty quantification into agent interactions.
• UA-ChatDev uses a lightweight uncertainty estimation mechanism based on token-level log probabilities to assess the confidence of agent responses.
• It also employs phase-aware threshold calibration to trigger retrieval-based verification when uncertainty exceeds acceptable levels.
• Experiments on the SRDD benchmark show that UA-ChatDev outperforms other single-agent and multi-agent software development frameworks in terms of completeness, executability, consistency, and overall quality.
• Additional ablation studies and communication analyses confirm that uncertainty-aware interactions enhance code execution reliability.

Key Concepts Mentioned

• software development
• multi-agent collaboration
• autonomous frameworks
• role-based collaboration
• uncertaintly quantification
• hallucination propagation
• UA-ChatDev
• SRDD benchmark

Claims Made by the Document

• Existing autonomous multi-agent software development frameworks assume equal reliability of intermediate agent outputs.
• UA-ChatDev consistently outperforms existing single-agent and multi-agent software development frameworks on various quality metrics.
• Uncertainty-aware interactions in UA-ChatDev enhance code execution reliability.

Evidence Used

• The text describes how existing approaches assume equal reliability (line 4)
• The paper reports results from experiments that demonstrate better performance of UA-ChatDev (lines 20-21)
• The text states that ablation studies and communication analyses verify the enhancement of code execution reliability due to uncertainty-aware interactions (line 25)

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Software development is a complex task that demands cooperation among agents with diverse roles. Large language models (LLMs) have enabled autonomous multi-agent software development frameworks that leverage role-based collaboration to automate requirements analysis, coding, testing, and refinement. However, existing approaches typically assume that intermediate agent outputs are equally reliable, leaving them vulnerable to hallucination propagation, where incorrect decisions generated in early development phases are transferred to downstream agents and negatively impact final software quality. To address this challenge, we propose UA-ChatDev, an uncertainty-aware multi-agent software development framework that integrates uncertainty quantification into agent interactions. It introduces a lightweight uncertainty estimation mechanism based on token-level log probabilities to assess the confidence of agent responses and employs phase-aware threshold calibration to selectively trigger retrieval-based verification when uncertainty exceeds acceptable levels. Extensive experiments on the SRDD benchmark demonstrate that UA-ChatDev consistently outperforms existing single-agent and multi-agent software development frameworks across completeness, executability, consistency, and overall quality metrics. Further ablation studies and communication analyses verify that uncertainty-aware interactions enhance code execution reliability.