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

2026-01-03

arXiv.org

AI Summary

This work addresses the challenge that large language models often generate inaccurate or incomplete exception-handling code in warehouse-scale software due to insufficient awareness of contextual and dependency information. To this end, the authors propose CatchAll, a novel approach that systematically integrates API-level exception knowledge, intra-repository calling context, and cross-repository reusable patterns to construct structured prompts that guide large language models toward generating precise exception-handling code. CatchAll achieves multi-granular knowledge injection through API–exception mapping, call trace modeling, and cross-project pattern mining. Evaluated on the newly curated benchmark RepoExEval, CatchAll substantially outperforms existing methods, achieving a CodeBLEU score of 0.31, an intent prediction accuracy of 60.1%, and a Pass@1 rate of 29%.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• exception handling
• repository-aware
• large language models
• code generation
• contextual constraints

Innovation

• repository-aware exception handling
• knowledge-guided LLMs
• API-exception mapping
• exception propagation
• structured prompting