Steady State Determination for Multiple Output Metrics
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Solution Overview
Problem
Current software performance testing tools lack the ability to reliably determine steady state in multiple output metrics, leading to inconsistent and unreliable test results due to subjective determination by performance test engineers and the absence of built-in measurement tools for steady-state analysis.
Innovation Solution
A processor-implemented method and system for determining steady state in web-based applications by calculating output metrics within micro, macro, and global time windows, providing an overall steadiness indication through a scorecard matrix and automated tools for steady state determination across three levels of temporal granularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If performance testing is conducted using industry standard tools without built-in steady state determination, then the tools can measure output metrics, but the determination of steady state becomes subjective and unreliable
Solution Approach 1:
The performance testing system performs self-assessment by automatically determining steady state for multiple output metrics using computational algorithms. The system analyzes its own test data to identify when steady state is reached, eliminating the need for external expert judgment and ensuring consistent, objective results.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring output metrics and using computational models to assess whether steady state has been achieved. The feedback loop allows the system to adjust measurement windows and determine steady state based on actual system behavior rather than predetermined assumptions.
2Ease of manufacture
If the entire experiment duration is used as a single time interval for average computation, then the implementation is simple, but the results may be inaccurate if steady state is not reached
Solution Approach 1:
The experiment duration is segmented into multiple time intervals (micro, macro, and global windows) rather than treating it as a single block. This segmentation allows the system to analyze steady state at different granularities and identify when metrics stabilize, improving measurement precision while maintaining computational feasibility.
3Adaptability or versatility
If multiple output metrics are measured simultaneously, then comprehensive performance assessment is achieved, but determining overall steady state becomes complex when metrics reach steady state at different times
Solution Approach 1:
The system introduces a temporal dimension by analyzing metrics across multiple time windows (micro, macro, global) rather than a single time point. This dimensional approach allows the system to handle metrics that reach steady state at different times by evaluating their behavior across the time spectrum and determining overall steady state based on the collective behavior of all metrics.
4Reliability
If manual analysis by performance test engineers is used for steady state determination, then expert judgment can be applied, but the process is time-consuming and subjective
Solution Approach 1:
The manual mechanical process of expert analysis is replaced with an automated computational system that uses algorithms to determine steady state. The system substitutes human experts with machine-based computational models that can rapidly and objectively assess whether metrics have reached steady state, eliminating subjectivity and reducing time consumption.
Data Source
AI summary
This disclosure relates generally to software performance testing, and more particularly to a system and method for steady state performance testing of a multiple output software system. According to one exemplary embodiment, a processor-implemented performance test for steady-state determination method is described. The method may include executing, via one or more hardware processors, a performance test of a web-based application, calculating, via the one or more hardware processors, a plurality of output metrics based on the performance test, determining, via the one or more hardware processors, whether each of the output metrics has achieved steady state within micro, macro, and global initial time windows, and providing an overall steadiness indication based on the determination of whether each of the output metrics has achieved steady state within the micro, macro, and global time windows.