🤖 AI Summary
Large language models (LLMs) frequently generate code lacking robust exception handling, leading to runtime fragility. To address this, we propose Seeker—a novel multi-agent framework that systematically orchestrates LLMs across the full exception-handling pipeline: detection, exception type identification, and repair generation. Seeker comprises five specialized agents—Scanner, Detector, Predator, Ranker, and Handler—that jointly integrate static analysis, exception pattern mining, and ranking-enhanced repair generation. This design mitigates three critical challenges: inaccurate fragile-code identification, erroneous exception-type classification, and semantically distorted repairs. Evaluated on multiple open-source projects, Seeker achieves a 37.2% improvement in exception-handling coverage, an 89.5% accuracy in exception-type identification, and an over-82% compilation success rate for generated fixes—demonstrating significant advances in automated, LLM-driven exception handling.
📝 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 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.