论文标题
通过加固学习自动分阶段推出
Automating Staged Rollout with Reinforcement Learning
论文作者
论文摘要
上演的推出是将软件更新逐渐发布到用户群体的策略，以便加速缺陷发现而不会产生灾难性的结果，例如系统中断。过去的一些研究检查了如何量化和自动化的分期推出，但没有同时考虑多个产品或过程指标。本文展示了通过多目标增强学习自动化的分阶段推出的潜力，以动态平衡利益相关者的需求，例如时间来交付新功能和由于潜在缺陷而导致的故障产生的停机时间。
Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Some past studies have examined how to quantify and automate staged rollout, but stop short of simultaneously considering multiple product or process metrics explicitly. This paper demonstrates the potential to automate staged rollout with multi-objective reinforcement learning in order to dynamically balance stakeholder needs such as time to deliver new features and downtime incurred by failures due to latent defects.
