  Modern code review is a critical quality assurance process that is widely
adopted in both industry and open source software environments. This process
can help newcomers learn from the feedback of experienced reviewers; however,
it often brings a large workload and stress to reviewers. To alleviate this
burden, the field of automated code reviews aims to automate the process,
teaching large language models to provide reviews on submitted code, just as a
human would. A recent approach pre-trained and fine-tuned the code intelligent
language model on a large-scale code review corpus. However, such techniques
did not fully utilise quality reviews amongst the training data. Indeed,
reviewers with a higher level of experience or familiarity with the code will
likely provide deeper insights than the others. In this study, we set out to
investigate whether higher-quality reviews can be generated from automated code
review models that are trained based on an experience-aware oversampling
technique. Through our quantitative and qualitative evaluation, we find that
experience-aware oversampling can increase the correctness, level of
information, and meaningfulness of reviews generated by the current
state-of-the-art model without introducing new data. The results suggest that a
vast amount of high-quality reviews are underutilised with current training
strategies. This work sheds light on resource-efficient ways to boost automated
code review models.
