Neural Exception Handling Recommender

Document Type

Conference Proceeding

Publication Date

4-14-2024

Abstract

Practical code reuse often leads to the incorporation of code fragments from developer forums into applications. However, these fragments, being incomplete, frequently lack details on exception handling. Integrating exception handling into a codebase is not a straightforward task, requiring developers to understand and remember which API methods may trigger exceptions and which exceptions should be handled. To address that, we introduce EHBlock, a learning-based exception handling recommender for Java code snippets. EHBlock analyzes a given code snippet and suggests whether a try-catch block is necessary. It employs a Relational Graph Convolutional Network (R-GCN) to learn exception handling from complete code. R-GCN considers program dependencies in the surrounding context, allowing EHBlock to learn the identities of APIs and their relations with corresponding exception types that need to be handled. Our empirical evaluation shows that EHBlock achieves a 12.3% improvement in F-score compared to the state-of-the-art approach in determining the need of try-catch blocks.

Identifier

85194814002 (Scopus)

ISBN

[9798400705021]

Publication Title

Proceedings - International Conference on Software Engineering

External Full Text Location

https://doi.org/10.1145/3639478.3643082

ISSN

02705257

First Page

316

Last Page

317

Grant

CNS-2120386

Fund Ref

National Science Foundation

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