Seeker: Towards Exception Safety Code Generation with Intermediate
  Language Agents Framework
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.
6762f1ae42436c7f70b76849
Xuanming Zhang
claimed_verified
2024-12-19T18:01:56.621Z
65fc5109899083a2aad987c5
/avatars/289dbb8128746d931118cff6f6871a45.svg
XUANMING ZHANG
user
XUANMINGZHANG
6762f1ae42436c7f70b7684a
Yuxuan Chen
6762f1ae42436c7f70b7684b
Yiming Zheng
claimed_verified
2025-01-01T20:13:54.124Z
63d9ce35aefaa735258607e2
/avatars/b93d4acb519a8bebe14494ebe113b0b8.svg
Yiming Zheng
user
ZYM666
6762f1ae42436c7f70b7684c
Zhexin Zhang
6762f1ae42436c7f70b7684d
Yuan Yuan
6762f1ae42436c7f70b7684e
Minlie Huang
Seeker: Towards Exception Safety Code Generation with Intermediate
  Language Agents Framework
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.
Seeker, a multi-agent framework utilizing LLMs, addresses common exception handling issues to enhance code robustness in real-world software development.
large language models (LLMs) exception handling fragile code exception block handling solution multi-agent framework Scanner Detector Predator Ranker Handler