On Unified Prompt Tuning for Request Quality Assurance in Public Code Review
<|reference_start|>On Unified Prompt Tuning for Request Quality Assurance in Public Code Review: 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.<|reference_end|>
@article{chen2024on,
    title={On Unified Prompt Tuning for Request Quality Assurance in Public Code
  Review},
    author={Xinyu Chen, Lin Li, Rui Zhang, Peng Liang},
    journal={arXiv preprint arXiv:2404.07942},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07942},
    primaryClass={cs.SE cs.AI}
}