Safely and Quickly Deploying New Features with a Staged Rollout Framework Using Sequential Test and Adaptive Experimental Design
| Autor: | Zhenyu Zhao, Mandie Liu, Anirban Deb |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Code review business.industry Real-time computing Feature extraction Process (computing) 02 engineering and technology computer.software_genre Statistics - Applications Automation Change management (ITSM) User experience design Feature (computer vision) 020204 information systems Sequential probability ratio test 0202 electrical engineering electronic engineering information engineering Applications (stat.AP) 020201 artificial intelligence & image processing business computer |
| Zdroj: | 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA). |
| DOI: | 10.1109/iccia.2018.00019 |
| Popis: | During the rapid development cycle for Internet products (websites and mobile apps), new features are developed and rolled out to users constantly. Features with code defects or design flaws can cause outages and significant degradation of user experience. The traditional method of code review and change management can be time-consuming and error-prone. In order to make the feature rollout process safe and fast, this paper proposes a methodology for rolling out features in an automated way using an adaptive experimental design. Under this framework, a feature is gradually ramped up from a small proportion of users to a larger population based on real-time evaluation of the performance of important metrics. If there are any regression detected during the ramp-up step, the ramp-up process stops and the feature developer is alerted. There are two main algorithm components powering this framework: 1) a continuous monitoring algorithm - using a variant of the sequential probability ratio test (SPRT) to monitor the feature performance metrics and alert feature developers when 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 algorithms including time-based, power-based, and risk-based (a Bayesian approach) schedules. These algorithms are evaluated and compared on both simulated data and real data. There are three benefits provided by this framework for feature rollout: 1) for defective features, it can detect the regression early and reduce negative effect, 2) for healthy features, it rolls out the feature quickly, 3) it reduces the need for manual intervention via the automation of the feature rollout process. |
| Databáze: | OpenAIRE |
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