Seeker: Enhancing Exception Handling in Code with a LLM-based Multi-Agent Approach

Xuanming Zhang, Yuxuan Chen, Yuan Yuan, Minlie Huang

Keywords: Alignment, Code Generation, Code Robustness, Agent Framework, RAG

Abstract:

In real-world software development, improper or missing exception handling can severely impact the robustness and reliability of code. Exception handling mechanisms require developers to detect, capture, and manage exceptions according to high standards, but many developers struggle with these tasks, leading to fragile code. This problem is particularly evident in open-source projects and impacts the overall quality of the software ecosystem.
To address this challenge, we explore the use of large language models (LLMs) to improve exception handling in code. Through extensive analysis, we identify three key issues: Insensitive Detection of Fragile Code, Inaccurate Capture of Exception Types, and Distorted Handling Solutions. These problems are widespread across real-world repositories, suggesting that robust exception handling practices are often overlooked or mishandled.
In response, we propose \emph{Seeker}, a multi-agent framework inspired by expert developer strategies for exception handling. Seeker uses agents—Scanner, Detector, Predator, Ranker, and Handler—to assist LLMs in detecting, capturing, and resolving exceptions more effectively. Our work is the first systematic study on leveraging LLMs to enhance exception handling practices, providing valuable insights for future improvements in code reliability.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 4229