CatchAll: Repository-Aware Exception Handling with Knowledge-Guided LLMs

TL;DR

CatchAll is a novel LLM-based approach that enhances exception handling in code by integrating API, repository context, and cross-project knowledge, leading to more accurate and context-aware error recovery.

Contribution

The paper introduces CatchAll, a repository-aware exception handling method that combines multiple knowledge layers to improve LLM performance in real-world codebases.

Findings

Outperforms state-of-the-art baselines in code generation metrics.

Achieves higher intent prediction accuracy.

Demonstrates effectiveness on new benchmark datasets.

Abstract

Exception handling is a vital forward error-recovery mechanism in many programming languages, enabling developers to manage runtime anomalies through structured constructs (e.g., try-catch blocks). Improper or missing exception handling often leads to severe consequences, including system crashes and resource leaks. While large language models (LLMs) have demonstrated strong capabilities in code generation, they struggle with exception handling at the repository level, due to complex dependencies and contextual constraints. In this work, we propose CatchAll, a novel LLM-based approach for repository-aware exception handling. CatchAll equips LLMs with three complementary layers of exception-handling knowledge: (1) API-level exception knowledge, obtained from an empirically constructed API-exception mapping that characterizes the exception-throwing behaviors of APIs in real-world codebases; (2) repository-level execution context, which captures exception propagation by modeling contextual call traces around the target code; and (3) cross-repository handling knowledge, distilled from reusable exception-handling patterns mined from historical code across projects. The knowledge is encoded into structured prompts to guide the LLM in generating accurate and context-aware exception-handling code. To evaluate CatchAll, we construct two new benchmarks for repository-aware exception handling: a large-scale dataset RepoExEval and an executable subset RepoExEval-Exec. Experiments demonstrate that RepoExEval consistently outperforms state-of-the-art baselines, achieving a CodeBLEU score of 0.31 (vs. 0.27% for the best baseline), intent prediction accuracy of 60.1% (vs. 48.0%), and Pass@1 of 29% (vs. 25%). These results affirm RepoExEval's effectiveness in real-world repository-level exception handling.