Contrastive Learning for Multi-Modal Automatic Code Review

TL;DR

This paper introduces a multi-modal dataset and a contrastive learning-based model for automatic code review that leverages both code and developer comments, improving prediction accuracy.

Contribution

It presents a new multi-modal dataset (MACR) and a contrastive learning framework (CLMN) that jointly encode code and comments for enhanced automatic code review.

Findings

CLMN outperforms existing methods on MACR dataset

Contrastive pre-training improves multi-modal encoding effectiveness

Utilizing developer comments enhances code review accuracy

Abstract

Automatic code review (ACR), aiming to relieve manual inspection costs, is an indispensable and essential task in software engineering. The existing works only use the source code fragments to predict the results, missing the exploitation of developer's comments. Thus, we present a Multi-Modal Apache Automatic Code Review dataset (MACR) for the Multi-Modal ACR task. The release of this dataset would push forward the research in this field. Based on it, we propose a Contrastive Learning based Multi-Modal Network (CLMN) to deal with the Multi-Modal ACR task. Concretely, our model consists of a code encoding module and a text encoding module. For each module, we use the dropout operation as minimal data augmentation. Then, the contrastive learning method is adopted to pre-train the module parameters. Finally, we combine the two encoders to fine-tune the CLMN to decide the results of Multi-Modal ACR. Experimental results on the MACR dataset illustrate that our proposed model outperforms the state-of-the-art methods.