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.

使用统一框架UniPCR来解决开发者需求质量保证（即预测需求的必要性和推荐标签子任务）的问题，该框架通过掩码语言模型（MLM）改进了文本提示调参和代码前缀调参，实现了两个子任务的自动化完成，并在公共代码评审数据集上展示了高效性。

关于公共代码审查中的请求质量保证的统一提示调整