Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent
  Approach
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.
67073844d2a6eeb542ceb2fe
Xuanming Zhang
claimed_verified
2024-10-10T08:01:38.371Z
65fc5109899083a2aad987c5
/avatars/289dbb8128746d931118cff6f6871a45.svg
XUANMING ZHANG
user
XUANMINGZHANG
67073844d2a6eeb542ceb2ff
Yuxuan Chen
67073844d2a6eeb542ceb300
Yuan Yuan
67073844d2a6eeb542ceb301
Minlie Huang
Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent
  Approach
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.
large language models (LLMs) exception handling fragile code exception types handling solutions Seeker multi-agent framework scanners detector predator ranker handler