Automating Staged Rollout with Reinforcement Learning
This addresses the challenge of safely releasing software updates for developers and organizations, but it appears incremental as it builds on prior work by explicitly considering multiple metrics.
The paper tackled the problem of automating staged rollout of software updates by using multi-objective reinforcement learning to dynamically balance stakeholder needs like feature delivery time and downtime from defects, resulting in a method that optimizes these metrics without specifying concrete numerical improvements.
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.