Real-Time Application Anomaly Detection via Feature Segmentation
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Solution Overview
Problem
As software applications become increasingly complex, it is difficult for developers to determine the effectiveness of updates in terms of operational performance and user experience, particularly when multiple new features are deployed, as it is challenging to identify which feature is contributing to poor performance or user experience.
Innovation Solution
A system architecture that includes an application monitoring and configuration server, which uses event tracking method calls to collect and attribute metrics from end user systems, allowing for real-time anomaly detection and remediation by statistically analyzing the impact of feature treatments on key performance metrics, and automatically configuring or rolling back features that cause degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple new features are deployed in an application, then the application functionality and user experience are enhanced, but it becomes difficult to identify which feature is contributing to poor performance
Solution Approach 1:
The patent segments the application into multiple feature treatments and tracks metrics for each feature separately. By dividing the overall application performance into attributable segments (individual features), the system can identify which specific feature is causing performance degradation while maintaining the ability to deploy multiple features simultaneously.
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors application metrics and attributes them to specific feature treatments. This feedback loop enables real-time detection of performance issues and automatic identification of problematic features, allowing developers to make informed decisions about which features to modify or remove.
2Reliability
If application updates are deployed to improve operational performance, then user experience may be enhanced, but it becomes difficult to measure whether the update is effective
Solution Approach 1:
The patent establishes a feedback system that continuously collects application metrics before and after updates are deployed. By comparing metrics across different feature treatments and time periods, the system can precisely measure whether an update has improved or degraded operational performance, providing actionable insights for developers.
Solution Approach 2:
The patent performs preliminary actions by establishing baseline metrics and control groups before deploying updates. This allows for accurate comparison and measurement of update effectiveness by having pre-existing data points to compare against the post-update performance.
3Adaptability or versatility
If application complexity increases with more user interface elements and functional elements, then the application capabilities are enhanced, but measuring effectiveness of updates becomes increasingly difficult
Solution Approach 1:
The patent segments the complex application into discrete feature treatments, each with its own metric attribution. This segmentation approach allows the system to manage and measure complex applications by breaking them down into measurable, attributable components rather than treating the entire application as a single unit.
Solution Approach 2:
The patent introduces an intermediary system (the monitoring and analysis platform) that mediates between the complex application and the measurement process. This intermediary automatically collects, attributes, and analyzes metrics, simplifying the measurement process despite the application's increased complexity.
4Loss of time
If real-time anomaly detection is implemented, then performance degradation can be quickly identified, but the system requires sophisticated monitoring and analysis capabilities
Solution Approach 1:
The patent implements a self-service monitoring system that automatically collects, attributes, and analyzes metrics without requiring manual intervention. The system autonomously detects anomalies, identifies problematic features, and provides recommendations, reducing the need for complex manual monitoring processes while maintaining real-time detection capabilities.
Solution Approach 2:
The patent uses automated feedback loops that continuously monitor application performance and immediately alert developers to anomalies. This real-time feedback mechanism reduces detection time by automatically processing metrics and identifying issues as they occur, rather than requiring periodic manual reviews.
Data Source
AI summary
A method and apparatus for application anomaly detection and remediation is described. The method may include receiving a plurality of event tracking messages generated by configurable applications after a feature treatment is deployed to configurable applications running on a first set of end user systems, and associating the feature treatment with values of a metric from the event messages that are attributable to the feature treatment being executed by the one or more configurable applications. The method may also include determining an impact of the feature treatment on the degradation of the metric when compared to a control value of the metric determined from a second plurality of end user systems that are not exposed to the feature treatment. Then method may further include, in response to detecting the statistically significant degradation of the metric, performing one or more actions to remediate the undesired impact of the feature treatment on the execution of the application.