Seeker: Towards Exception Safety Code Generation with Intermediate
  Language Agents Framework
Authors: name: Xuanming Zhang, name: Yuxuan Chen, name: Yiming Zheng, name: Zhexin Zhang, 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
Block, and Distorted Handling Solution. 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 in real development scenarios, providing
valuable insights for future improvements in code reliability.