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