Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent
  Approach
Authors: name: Xuanming Zhang, name: Yuxuan Chen, name: Yuan Yuan, name: Minlie Huang

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 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.