Real-Time Application Error Identification Using Simulation
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Solution Overview
Problem
Current systems for testing and addressing errors in web applications are inadequate, often failing to identify all potential errors and resulting in a significant delay between user experience of errors and developer notification, which can be hours, days, or weeks.
Innovation Solution
A system that utilizes a simulation environment with historical raw user data to identify errors, a system analyzer to parse and correlate log entries, and an error predictor to alert developers and take remedial actions, with a knowledge base to record and retrieve error information for both simulation and live environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing with specifically designed test data is used, then testing can be performed, but error identification is inadequate and may not be comprehensive as to errors that may be experienced in real-world usage
Solution Approach 1:
The system performs preliminary error identification by analyzing historical user data and simulating user interactions before errors occur in production. The error identification module proactively identifies potential errors by correlating historical logs with current application state, allowing developers to fix issues before they affect real users.
Solution Approach 2:
The system creates a simulated environment that copies real-world usage patterns by ingesting historical user data and reproducing those interactions in a test environment. This allows comprehensive error identification without needing to test every possible real-world scenario manually.
2Loss of time
If manual error reporting by users is relied upon, then errors can be detected, but there is a significant time lag between user experience of errors and developer notification, which can be hours, days, or weeks
Solution Approach 1:
The system implements automated feedback loops where the error identification module continuously monitors application logs and user interactions, automatically detects errors, and immediately notifies developers through the user interface. This real-time feedback eliminates the delays inherent in manual user reporting.
Solution Approach 2:
The system performs self-diagnosis by automatically analyzing application logs and identifying errors without requiring user intervention. The error identification module autonomously correlates historical data with current errors and triggers notifications, enabling the system to serve itself in error detection.
3Measurement precision
If extensive testing and manual monitoring are performed, then more errors can be identified, but the process is time-consuming and delays remedial action
Solution Approach 1:
The system replaces manual mechanical processes of error detection with automated computational analysis. The error identification module uses algorithms to automatically correlate historical logs with current application state, providing precise error detection without the time-consuming manual analysis that would otherwise be required.
Data Source
AI summary
A method may be performed by a network server and includes: applying historical raw user data as an input to a first application running in a simulation environment; receiving activity log entries from the first application in the simulation environment; identifying a first group of the activity log entries corresponding to an error of the first application; saving an error entry in non-volatile storage media in response to identifying the first group of the activity log entries, the error entry including the first group of the activity log entries and an identification of action to be taken in response to the error of the first application; and causing a monitoring system to generate a first alert corresponding to the error entry.