Test Plan Generation Using Product Usage Data
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Solution Overview
Problem
Conventional test plans for computer products are often created manually based on assumptions about user behavior, which may not accurately reflect real-life usage scenarios, leading to missed test cases and inefficiencies due to resource constraints, and do not allow for updates based on changing user needs or real-life usage data.
Innovation Solution
A data-driven approach is implemented to generate test plans by obtaining usage data from various sources, identifying relevant parameters and values, comparing them to existing test plans, and modifying or adding test cases to reflect actual user scenarios, with the ability to automate test execution and create virtual test environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If test plans are created manually based on assumptions about user behavior, then the testing process can be initiated, but the test cases may not accurately reflect real-life usage scenarios, leading to missed test cases and reduced testing accuracy
Solution Approach 1:
The system implements feedback by continuously collecting real usage data from production environments and automatically incorporating it into test plan updates. This closed-loop approach ensures test cases reflect actual user behavior patterns, improving testing accuracy without requiring manual analysis of usage data.
Solution Approach 2:
The test planning system performs self-service by automatically generating, updating, and maintaining test cases based on collected usage data. The system autonomously identifies new test scenarios, updates existing test cases, and manages test plan versions without requiring manual intervention, thereby improving accuracy while managing complexity.
2Reliability
If comprehensive test cases covering all possible user scenarios are created, then testing coverage is improved, but resource constraints make it inefficient and time-consuming
Solution Approach 1:
The system applies partial action by focusing testing efforts on the most critical and frequently occurring usage scenarios identified from real data. Rather than attempting to manually create and manage all possible test cases, the system prioritizes high-impact test cases that cover the majority of actual user behavior, improving both coverage and efficiency.
Solution Approach 2:
The system dynamically adjusts test parameters based on collected usage data, such as frequency of specific operations, combinations of features used together, and environmental configurations. This allows the system to adaptively prioritize and execute the most relevant test cases, achieving comprehensive coverage for critical scenarios while maintaining high productivity.
3Adaptability or versatility
If test plans are updated frequently to reflect changing user needs and real-life usage data, then testing relevance is improved, but the manual update process increases time consumption and reduces adaptability
Solution Approach 1:
The system implements continuous action by automatically and continuously collecting usage data from production environments and seamlessly integrating it into test plan updates. This continuous feedback loop ensures test plans remain current with changing user needs without discrete manual update cycles, improving adaptability while eliminating time losses associated with manual updates.
Solution Approach 2:
The system performs preliminary action by proactively collecting and analyzing usage data before test plan updates are needed. By continuously monitoring real-world usage patterns and pre-processing this data, the system is ready to immediately generate updated test cases when changes are detected, reducing the time required for adaptations.
4Ease of operation
If manual creation and maintenance of test plans is performed, then flexibility in customization is maintained, but resource constraints lead to inefficiencies and increased manual effort
Solution Approach 1:
The system enables self-service by automatically performing test plan creation, updating, and maintenance tasks that would otherwise require manual effort. The system autonomously collects usage data, generates test cases, and manages test plans, dramatically reducing the energy and time investment required from human operators while maintaining flexibility through configurable parameters.
Solution Approach 2:
The system achieves universality by providing a multi-functional automated platform that handles data collection, analysis, test case generation, update management, and execution coordination. This single automated system replaces multiple manual processes, reducing overall manual effort while maintaining the ability to customize and adapt to different testing requirements.
Data Source
AI summary
Implementations for generating test plans for testing computer products based on product usage data are described. An example method may include obtaining, by a processing device, data associated with usage of a computer product, identifying, from the obtained data, a first set of parameters relevant to testing the computer product and a first set of values corresponding to the first set of parameters, comparing, by the processing device, the first set of parameters and the first set of values to a second set of parameters and a second set of values associated with a test plan to test the computer product, and generating, by the processing device, a modified version of the test plan in view of the comparison.