Auto-Complete Pattern Identification for Load Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current application performance validation tools fail to accurately emulate auto-complete communication patterns, which are crucial for realistic load testing and user experience simulation.
Innovation Solution
A pattern identification system that groups request entities into clusters based on criteria like request method, domain, and key-value pairs, and then analyzes these clusters to identify auto-complete communication patterns by dividing them into chains and removing irrelevant clusters, ensuring that load testing scripts accurately replicate desired communication patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If application performance tools use traditional load testing scripts, then they can apply consistent and measurable loads to applications, but they fail to accurately emulate auto-complete communication patterns that occur in production environments
Solution Approach 1:
The patent captures actual communication patterns from production environments by recording request entities exchanged between clients and servers. These recorded patterns are then copied and reused in load testing scripts, ensuring accurate replication of real-world auto-complete communication behaviors rather than relying on simplified traditional scripts.
Solution Approach 2:
The system analyzes captured communication patterns to identify auto-complete patterns and feeds this information back into the load testing script generation process. This feedback loop enables the creation of more accurate test scripts that reflect actual production communication patterns, continuously improving emulation accuracy.
2Ease of operation
If load testing scripts use simplified communication patterns, then they are easier to implement and execute, but they do not provide realistic user experience simulation
Solution Approach 1:
The system performs preliminary capture and analysis of communication patterns from production environments before executing load tests. By pre-processing and storing these patterns in a reusable format, the system maintains ease of operation while ensuring high realism in the load testing simulation.
3Measurement precision
If application performance tools capture and analyze detailed communication patterns from production, then they can identify accurate auto-complete patterns, but the complexity of pattern analysis increases
Solution Approach 1:
The pattern analysis system segments the complex task of identifying auto-complete patterns into distinct modules: capturing request entities, grouping them into clusters based on similarity, analyzing clusters to identify patterns, and generating test scripts. This segmentation reduces overall system complexity while maintaining high identification accuracy.
Solution Approach 2:
The patent introduces an intermediary clustering mechanism that groups similar request entities together before detailed pattern analysis. This intermediary step simplifies the analysis by reducing the number of individual patterns that need to be processed, thereby reducing system complexity while preserving identification accuracy.
Data Source
AI summary
A method for identifying an auto-complete communication pattern within a sequence of request entities includes grouping the request entities into a plurality of clusters according to a criterion. Clusters are removed from the plurality according to at least one of pattern analysis, a cluster size, and a cluster timing. Remaining clusters are identified as having an auto-complete communication pattern.