Experiment Platform Engine for A/B Test Analysis
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Solution Overview
Problem
Existing experiment platform engines are limited in configurability, processing speed, and throughput, making them ill-suited for analyzing diverse A/B tests, which can lead to delays and reduced efficiency in data processing and analysis.
Innovation Solution
A computerized method and system for controlled testing that includes providing a design specification for event data, retrieving and filtering testing events, applying metrics, and generating output data, allowing for customizable and efficient analysis of A/B test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing experiment platform engines are used to process diverse A/B tests, then basic processing capability is maintained, but configurability is limited and processing speed decreases
Solution Approach 1:
The system employs dynamic configuration where processing pipelines can be adjusted and reconfigured based on the specific requirements of different A/B tests. The configurable parameters allow the engine to adapt its processing logic, filtering criteria, and aggregation methods dynamically, enabling high configurability without sacrificing processing speed through rigid static structures.
Solution Approach 2:
The experiment platform engine utilizes parameter changes to resolve the contradiction by allowing flexible modification of processing parameters such as event filtering criteria, metric calculation methods, and data aggregation settings. These parameter adjustments enable the system to be configured for different test types while maintaining optimized processing performance through parameter tuning rather than structural redesign.
2Adaptability or versatility
If existing experiment platform engines process multiple A/B tests with variations, then coverage is maintained, but throughput is reduced due to delays
Solution Approach 1:
The processing system is segmented into independent modular components that can handle different A/B test variations simultaneously. Each module processes specific aspects of event data (filtering, aggregation, metric calculation) independently, allowing parallel processing of multiple test types without sequential delays, thereby maintaining high throughput while handling diverse test configurations.
Solution Approach 2:
The system performs preliminary configuration and setup of processing pipelines before A/B tests begin. Event filters, metric definitions, and aggregation rules are pre-configured based on expected test types, allowing the engine to immediately process test data without delays for ad-hoc configuration. This preliminary action enables the system to handle diverse test variations efficiently while maintaining high throughput.
Data Source
AI summary
A computerized method for controlled testing, comprising: providing a design specification for event data, the design specification including at least one of an event data source, an event data filter, and an event format; retrieving testing metrics; configuring testing events based on the design specification, by: retrieving testing events from the event data source specified in the design specification; filtering the retrieved testing events based in the design specification; and formatting the filtered testing events based on the design specification; generating analysis data by applying testing metrics to the configured event data; generating output data based on testing rules; and generating a user interface to display the output data.