Seeker: Towards Exception Safety Code Generation with Intermediate Language Agents Framework
Xuanming Zhang, Yuxuan Chen, Yiming Zheng, Zhexin Zhang, Yuan Yuan,, Minlie Huang

TL;DR
This paper introduces Seeker, a multi-agent framework leveraging large language models to improve exception handling in code, addressing common issues of fragility and inaccuracies in real-world software development.
Contribution
It presents the first systematic study on using LLMs with a multi-agent approach to enhance exception safety in practical coding environments.
Findings
Identified widespread issues in exception handling in real repositories.
Proposed a novel multi-agent framework inspired by expert developer strategies.
Demonstrated improved detection and resolution of exceptions using Seeker.
Abstract
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…
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Taxonomy
TopicsSafety Systems Engineering in Autonomy · Software Reliability and Analysis Research · Software Testing and Debugging Techniques
