Idle Testing Period Detection via Data Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing application testing methods fail to accurately identify idle testing periods, where both the test and the application under test are idle, leading to wasted time and inefficiencies due to undefined or incorrectly set timeouts.
Innovation Solution
A system that captures and correlates data points from test executions, including application and test actions, to determine idle testing periods by analyzing the absence of specific data types and using a directed acyclic graph (DAG) to identify periods where both the test and application are idle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional testing methods with undefined timeouts are used, then the testing process is simple to implement, but idle testing periods are not accurately identified leading to wasted time
Solution Approach 1:
The testing process is segmented into discrete data points representing individual test actions and application actions. Each data point is captured and correlated separately, allowing precise identification of idle periods between actions. This segmentation enables accurate measurement of idle testing periods without requiring complex overall system redesign.
Solution Approach 2:
The system implements feedback by continuously monitoring and correlating data points from both the test execution and application execution. This feedback mechanism allows the system to dynamically identify idle testing periods based on actual observed behavior rather than predetermined timeouts, reducing wasted time while maintaining manageable complexity through automated analysis.
2Measurement precision
If data correlation analysis is implemented to identify idle periods, then idle testing periods are accurately identified, but the complexity of data processing increases
Solution Approach 1:
A data correlation engine acts as an intermediary component that receives data points from both test and application executions. This intermediary processes and correlates the data to identify idle testing periods, separating the complexity of data analysis from the core testing functionality. The intermediary approach enables precise measurement while maintaining system modularity and managing complexity through dedicated processing logic.
3Productivity
If built-in timeouts are used in automated testing, then the testing process is straightforward, but the timeouts may overcompensate or undercompensate for actual test execution time
Solution Approach 1:
The system captures data points and establishes baseline timing information during initial test executions. This preliminary data collection enables the system to learn actual execution times and use this information to optimize timeout values in future tests. By performing preliminary action to gather timing data, the system improves both productivity through optimized timeouts and measurement precision through data-driven timeout accuracy.
Solution Approach 2:
The system dynamically adjusts timeout parameters based on correlated data from actual test and application execution. Rather than using fixed timeout values, the system modifies timeout parameters to match observed execution patterns, improving productivity by reducing unnecessary waiting time while maintaining measurement precision through adaptive parameter tuning based on empirical data.
Data Source
AI summary
Example implementations relate to determining idle testing periods. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also include an idle testing period determination engine to determine, based on the correlation of the data points, idle testing periods of the test executions. The idle testing periods may be periods of time where both the test executions and the application under test are idle.