  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.
