Dynamic Website Test Scripts for Pre-Launch Anomaly Detection
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Solution Overview
Problem
Current systems fail to comprehensively test websites under various system, network, and user interaction conditions, leading to undetected anomalies that cause malfunctions, security breaches, and errors when the website goes live.
Innovation Solution
A system that dynamically determines conditional metrics based on historical data and user interactions, generates test case scripts to emulate various conditions, and proactively detects and mitigates anomalies by updating the website's source code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive testing under multiple conditions is implemented, then website reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary testing under multiple conditions before the website goes live. Test scripts are generated and executed in advance to detect anomalies, allowing issues to be fixed before deployment. This preliminary action ensures reliability without requiring complex ongoing testing infrastructure.
Solution Approach 2:
The system uses historical data from the website itself to generate test scripts automatically. By analyzing existing user interactions and system conditions, the system creates its own test cases without external intervention, reducing the complexity of test script management while improving coverage.
2Measurement precision
If dynamic test script generation is implemented, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system generates test scripts in advance based on historical data before actual website deployment. By performing the script generation preliminary, the system achieves precise condition evaluation without time pressure during critical deployment phases.
Solution Approach 2:
The system creates test scripts that copy and emulate real user interactions and system conditions from historical data. Instead of manually designing each test case, the system replicates actual usage patterns, achieving high measurement precision efficiently.
3Reliability
If comprehensive condition metrics are collected, then reliability is improved, but use of energy increases
Solution Approach 1:
The system leverages existing historical data and logs from the website's own operation to generate test scripts. By using self-generated data rather than external test data, the system achieves comprehensive condition coverage without requiring additional energy-intensive data collection infrastructure.
Solution Approach 2:
The system focuses on collecting and analyzing the most critical condition metrics from historical data rather than attempting to measure every possible parameter. This partial action approach achieves sufficient reliability for anomaly detection while conserving computational energy.
Data Source
AI summary
A system for improving performance of a website is disclosed. The system detects web components associated with the website and determines conditional metrics. The conditional metrics indicate a range of conditions under which the performance of the website is evaluated. The system generates a set of test case scripts to emulate various user interactions with the website under various conditions according to one or more conditional metrics. The system executes a first test case script to emulate a first user interaction with a first web element under a first condition. The system determines that a result of the first test case script does not correspond to an expected output. In response, the system performs a corrective action, including updating a code portion associated with the first web element in the source code of the website to a code portion that is configured to provide the expected output.