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During the rapid development cycle for Internet products (websites and mobileapps), new features are developed and rolled out to users constantly. Featureswith code defects or design flaws can cause outages and significant degradationof user experience. The traditional method of code review and change managementcan be time-consuming and error-prone. In order to make the feature rolloutprocess safe and fast, this paper proposes a methodology for rolling outfeatures in an automated way using an adaptive experimental design. Under thisframework, a feature is gradually ramped up from a small proportion of users toa larger population based on real-time evaluation of the performance ofimportant metrics. If there are any regression detected during the ramp-upstep, the ramp-up process stops and the feature developer is alerted. There aretwo main algorithm components powering this framework: 1) a continuousmonitoring algorithm - using a variant of the sequential probability ratio test(SPRT) to monitor the feature performance metrics and alert feature developerswhen a metric degradation is detected, 2) an automated ramp-up algorithm -deciding when and how to ramp up to the next stage with larger sample size.This paper presents one monitoring algorithm and three ramping up algorithmsincluding time-based, power-based, and risk-based (a Bayesian approach)schedules. These algorithms are evaluated and compared on both simulated dataand real data. There are three benefits provided by this framework for featurerollout: 1) for defective features, it can detect the regression early andreduce negative effect, 2) for healthy features, it rolls out the featurequickly, 3) it reduces the need for manual intervention via the automation ofthe feature rollout process.

Safely and Quickly Deploying New Features with a Staged Rollout Framework Using Sequential Test and Adaptive Experimental Design