通过conidfine解决代码改进中的模两可:一个具有清晰度和有针对性的检索的对话式Aware框架
Aoyu Song1, Afizan Azman1, Shanzhi Gu2
1School of Computer Science, Taylor's University, Subang Jaya, 47500, Selangor, Malaysia.
概括
本研究引入了一种新的方法,通过使用开发者-审核员对话来解决模两可的代码审查评论. 它可以帮助开发人员更有效地理解和修复代码.
科学领域:
- 软件工程 软件工程 软件工程
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 代码改进在软件开发中至关重要.
- 模两可的审查评论阻碍了开发人员的理解和任务完成.
- 开发人员和审稿人之间的对话数据可以解决模两可的问题.
研究的目的:
- 通过使用对话数据,提出一种新的方法来解决模两可的代码审查建议.
- 为了使模型能够自主检测模两可,并提取相关的对话段以澄清.
主要方法:
- 开发了一种使用多任务学习进行模两可的分类和混点生成的模两可的区分器.
- 引入了一种类型驱动的多策略检索框架,以提取基于模糊类型的可操作信息.
- 构建了一个语义辅助指令库,以便在复习建议和对话上下文之间精确对齐.
主要成果:
- 在代码改进任务上显著提高了先进模型 (TransReview,T5-Review,CodeT5,CodeReviewer,ChatGPT) 的性能.
- 演示了对话信息在提高模型性能在细粒度模糊性解决方案方面的有效性.
- 人类评估证实了模糊性检测的准确性和生成的混点的正确性.
结论:
- 这项研究是第一个解决代码改进中模两可的审查建议的研究.
- 提出的方法有效地通过利用对话上下文来解决模两可的问题.
- 这些发现为未来的研究提供了基础,以提高代码审查的清晰度和有效性.
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