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