Time-Series Data Partitioning for Automated Software Testing
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Solution Overview
Problem
The testing of software applications is hindered by the diversity of devices, operating systems, and web browsers, leading to compatibility issues and inefficiencies in automated testing due to noisy test run data and the reliance on unsophisticated troubleshooting methods.
Innovation Solution
The method involves receiving time-series data for software applications, partitioning it using change-point detection to identify ranges conforming to probabilistic distributions, filtering outliers, and determining expected behavior for each partition to confidently evaluate test runs and identify meaningful changes in performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated testing is conducted across multiple devices, operating systems, and browsers, then testing coverage and reliability are improved, but test run data becomes noisy and harder to interpret
Solution Approach 1:
The patent segments test run data into distinct groups or clusters based on similarity metrics, separating meaningful performance patterns from noisy variations. By dividing the heterogeneous test data into homogeneous segments, the system can identify reliable performance indicators within each segment while filtering out cross-segment noise caused by device and environment diversity.
Solution Approach 2:
The patent introduces statistical analysis and pattern recognition algorithms as intermediaries between raw test data and interpretation. These intermediaries process the noisy multi-device test data, extracting meaningful performance signals while filtering out environmental noise, thereby preserving information quality across diverse testing environments.
2Measurement precision
If sophisticated analysis methods are implemented to filter test data noise, then measurement precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent employs dynamic analysis methods that adapt to the characteristics of test data from different devices and environments. The analysis system dynamically adjusts its parameters and thresholds based on observed data patterns, achieving high measurement precision without requiring overly complex fixed-rule systems. This dynamic approach allows the system to handle noise adaptively while maintaining computational efficiency.
Data Source
AI summary
Methods and apparatus are described by which time-series data captured during the automated testing of software applications may be analyzed. Change-point detection is used to partition the time-series data, and an expected variance of data within each partition is determined. Because the partitioning of the test data provides a high level of confidence that the data points in a given partition conform to the same distribution, data points that represent meaningful changes in application performance can be more confidently and efficiently identified.