Automated Transaction Response Time Measurement via Image Classifier
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Solution Overview
Problem
Manual measurement of end-to-end response time for software transactions is time-consuming and monotonous, especially in performance testing of modern software products with graphical user interfaces.
Innovation Solution
A transaction management system that uses an image classifier to detect the end of a transaction by analyzing screenshot images captured at fixed intervals, allowing for automated computation of transaction completion time without requiring resource-intensive browser tools or changes to the software source code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used to measure end-to-end response time, then measurement precision can be maintained, but productivity decreases significantly due to time-consuming and monotonous work
Solution Approach 1:
The system enables automated self-measurement of response time by capturing screenshots at transaction start and end points, automatically comparing images to detect transaction completion, and computing response time without human intervention. This transforms manual measurement into an autonomous process that maintains precision while dramatically improving productivity
Solution Approach 2:
The patent replaces manual mechanical measurement processes with an automated image-based detection system. Instead of human operators manually timing transactions, the system uses screenshot capture, image comparison algorithms, and automated timestamp extraction to measure response time, substituting mechanical human effort with automated computational processes
2Measurement precision
If resource-intensive browser tools or source code modifications are used to measure performance, then measurement capability is enhanced, but device complexity and resource usage increase
Solution Approach 1:
The patent extracts the essential measurement function from complex browser tools and source code instrumentation. By capturing only screenshots at key transaction points and comparing these images to detect completion, the system isolates the core measurement capability from unnecessary computational overhead, reducing resource requirements while maintaining measurement effectiveness
Solution Approach 2:
The system uses temporary, disposable screenshots as measurement carriers instead of persistent instrumentation code or heavy browser tools. Each screenshot is a lightweight, transient object used solely for detecting transaction state, eliminating the need for complex, resource-intensive measurement infrastructure
Data Source
AI summary
Software testing techniques based on image recognition are disclosed. In various embodiments, a programmatically implemented image classifier is trained to recognize a screen shot image as being associated with a transaction end condition of a transaction. A test script configured to initiate an iteration of the transaction is run. A start time of the iteration of the transaction is recorded. Screen shot images are generated during performance of the iteration of the transaction to capture a series of screen shot images of at least a portion of a user interface display associated with the iteration of the transaction. The image classifier is used to find an earliest-captured image that matches the transaction end condition. A time associated with the matched image is used as a transaction end time to compute an end-to-end time to perform the iteration of the transaction.