  Commit messages are important for software development and maintenance. Many
neural network-based approaches have been proposed and shown promising results
on automatic commit message generation. However, the generated commit messages
could be repetitive or redundant. In this paper, we propose RACE, a new
retrieval-augmented neural commit message generation method, which treats the
retrieved similar commit as an exemplar and leverages it to generate an
accurate commit message. As the retrieved commit message may not always
accurately describe the content/intent of the current code diff, we also
propose an exemplar guider, which learns the semantic similarity between the
retrieved and current code diff and then guides the generation of commit
message based on the similarity. We conduct extensive experiments on a large
public dataset with five programming languages. Experimental results show that
RACE can outperform all baselines. Furthermore, RACE can boost the performance
of existing Seq2Seq models in commit message generation.
