Analytics-Driven Web Application Testing via Real Usage Patterns
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Solution Overview
Problem
Current web application testing methods are resource-intensive and costly, limiting the number of browser/platform combinations that can be tested, as they rely on standardized or speculative user paths, which may not reflect real-world usage, and do not efficiently identify and correct errors or bugs.
Innovation Solution
An analytics-driven development system that records and correlates user interface operations with actions on a web application, transmitting this data to a test service to construct a test routine based on real-world usage patterns, allowing for automated and effective testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive testing of every possible path through all pages is performed, then testing completeness is improved, but testing cost and resource consumption increase prohibitively
Solution Approach 1:
Instead of testing every possible path through the website, the system uses analytics data to identify and test only the most frequently visited paths and pages. This partial action approach focuses testing resources on high-value areas that represent real user behavior, achieving sufficient testing completeness without the prohibitive cost of exhaustive testing.
Solution Approach 2:
The system incorporates feedback from analytics data about actual user behavior patterns to dynamically determine which paths and pages should be tested. By continuously learning from real usage data, the testing system can adapt to changing user behaviors and prioritize paths that matter most, improving testing effectiveness while reducing resource consumption.
2Productivity
If standardized test paths are used for testing, then testing efficiency is improved, but accuracy in reflecting real-world usage decreases
Solution Approach 1:
The system transitions from static, standardized test paths to dynamic test paths that are generated based on real analytics data. The test paths automatically adapt to reflect actual user behavior patterns, ensuring both efficiency through automation and precision in representing real-world usage scenarios.
Solution Approach 2:
The system changes the parameters of test path selection from fixed standardized sequences to variable paths determined by analytics data. By using metrics such as page view frequency, time spent on pages, and navigation patterns, the system generates test paths that accurately represent real-world usage while maintaining testing efficiency.
3Adaptability or versatility
If more browser/platform combinations are tested, then testing coverage is improved, but available resources decrease due to increased consumption
Solution Approach 1:
Instead of testing all possible browser and platform combinations exhaustively, the system uses analytics to identify which combinations are most relevant to actual user base. This allows the system to allocate resources to test the most important combinations first, achieving sufficient coverage without depleting available resources.
Data Source
AI summary
Analytics of the recorded user interface operations at clients accessing a web application at a server are used to develop a test routine for testing the web application at a cloud service. A code module, which is either at the server or attached to the browser in each of the clients, records and correlates each of the user interface operations at each client to each action taken in the web application as it is being navigated. The module then transmits the correlated user interface operations to the test service. At the test service, analytics are performed on the correlated user interface operations to develop data from the user interface operations, from which data the test routine is constructed.