Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2020

Abstract

Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and usesmultiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ICSE '20: Proceedings of the 42nd ACM/IEEE International Conference on Software Engineering: June 27 - July 19, Seoul

First Page

518

Last Page

529

ISBN

9781450371216

Identifier

doi.org/10.1145/3377811.3380361

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3377811.3380361

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