  Public Code Review (PCR) can be implemented through a Software Question
Answering (SQA) community, which facilitates high knowledge dissemination.
Current methods mainly focus on the reviewer's perspective, including finding a
capable reviewer, predicting comment quality, and recommending/generating
review comments. Our intuition is that satisfying review necessity requests can
increase their visibility, which in turn is a prerequisite for better review
responses. To this end, we propose a unified framework called UniPCR to
complete developer-based request quality assurance (i.e., predicting request
necessity and recommending tags subtask) under a Masked Language Model (MLM).
Specifically, we reformulate both subtasks via 1) text prompt tuning, which
converts two subtasks into MLM by constructing prompt templates using hard
prompt; 2) code prefix tuning, which optimizes a small segment of generated
continuous vectors as the prefix of the code representation using soft prompt.
Experimental results on the Public Code Review dataset for the time span
2011-2022 demonstrate that our UniPCR framework adapts to the two subtasks and
outperforms comparable accuracy-based results with state-of-the-art methods for
request quality assurance. These conclusions highlight the effectiveness of our
unified framework from the developer's perspective in public code review.
