学习提高现代代码审查的效率
Learning to Boost the Efficiency of Modern Code Review
摘要：
现代代码审查（MCR）是各种软件开发组织的标准。MCR通过在质量保证和知识转移方面的感知和证明的好处来支付自己。然而，在MCR上投入的时间通常是大量的。本论文的目标是通过开发人工智能技术来提高MCR的效率，这些技术可以部分取代或辅助人类评审员。设想的技术与现有的MCR相关AI模型的区别在于，我们将这些挑战解释为图学习问题。这应该允许我们使用该领域的科学算法，直接从现有项目中学习编码和审查标准。所需的训练数据将从在线存储库中挖掘出来，实验将设计成使用标准的定量评估指标。本研究计画明确了本论文的研究动机、研究问题与解决方案，并对相关工作进行了概述。
英文摘要：
Modern Code Review (MCR) is a standard in all kinds of organizations that develop software. MCR pays for itself through perceived and proven benefits in quality assurance and knowledge transfer. However, the time invest in MCR is generally substantial. The goal of this thesis is to boost the efficiency of MCR by developing AI techniques that can partially replace or assist human reviewers. The envisioned techniques distinguish from existing MCR-related AI models in that we interpret these challenges as graph-learning problems. This should allow us to use state-of-science algorithms from that domain to learn coding and reviewing standards directly from existing projects. The required training data will be mined from online repositories and the experiments will be designed to use standard, quantitative evaluation metrics. This research proposal defines the motivation, research-questions, and solution components for the thesis, and gives an overview of the relevant related work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
