ML-Based Test Template Selection and Filling
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Solution Overview
Problem
Current automated software testing tools are inefficient and prone to errors when dealing with complex calculations and navigation across multiple screens, requiring extensive subject matter expert involvement and manual template selection and filling, which leads to mistakes and false positives, and are performance-intensive for desktop applications.
Innovation Solution
A system and method that uses machine learning and natural language processing to automatically select and fill testing templates by interpreting test scenarios, eliminating the need for knowledge of business processes or testing tools, and accommodating changes in template versions by comparing new and old versions for updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated testing tools are used for complex calculations and multi-screen navigation, then automation coverage is achieved, but accuracy and reliability deteriorate due to errors and false positives
Solution Approach 1:
The patent introduces subject matter experts (SMEs) as intermediaries who manually select appropriate test templates based on their knowledge of business processes and application behavior. This intermediary step bridges the gap between automated testing tools and reliable test execution, allowing automation to cover complex scenarios while maintaining accuracy through human expertise in template selection.
Solution Approach 2:
The system enables SMEs to independently select and configure test templates without requiring deep knowledge of testing tool internals. The template library and selection interface allow SMEs to self-service the template selection process, reducing errors while maintaining automation benefits.
2Reliability
If subject matter experts manually select and fill test templates, then testing accuracy improves, but productivity and efficiency deteriorate due to time-consuming manual processes
Solution Approach 1:
Test templates are pre-configured with appropriate fields, data types, and validation rules based on common business processes and application patterns. This preliminary preparation allows SMEs to quickly select and populate templates without performing complex configuration tasks, maintaining accuracy while significantly reducing the time required for test setup.
Solution Approach 2:
The system provides reusable test templates that can be copied and adapted for similar testing scenarios. Once a template is created and validated, it can be reused across multiple test cases, eliminating the need to manually create templates from scratch each time and大幅提高 testing efficiency.
3Adaptability or versatility
If SMEs learn to adjust processes for new template versions, then adaptability improves, but ease of operation deteriorates due to continuous learning requirements
Solution Approach 1:
The system provides feedback mechanisms that notify SMEs of template version changes and guide them through necessary adjustments. The interface highlights modified fields and provides contextual help, allowing SMEs to adapt to new versions without requiring comprehensive retraining, thus maintaining ease of operation while ensuring adaptability to updates.
4Extent of automation
If desktop applications are tested using traditional automation tools, then testing capability is achieved, but system performance deteriorates due to CPU and memory intensity
Solution Approach 1:
The testing system is segmented into separate components: template selection, test execution, and result analysis. The template selection and configuration occur separately from the actual application execution, allowing the testing framework to be lightweight and not compete heavily for resources during the critical application testing phase, thus maintaining automation capability while reducing performance impact.
Data Source
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AI summary
A system and method that improves the efficiency and accuracy of template selection process by applying machine learning to perform natural language processing (NLP) to automatically interpret the intent of the test scenarios and to match the intent of the test scenarios to appropriate test template(s). The system and method can use keyphrases and part-of-speech (POS) tokens to accurately capture the intent of test scenarios and templates. The system and method can additionally use machine learning to perform NLP to identify information from the test scenario(s) that relates to fields in templates to automatically fill in fields in the selected test template(s). In situations where the processing of the test scenario(s) does not provide all of the information necessary to fill every field of a template, the system and method can use machine learning to perform NLP to automatically create and execute a search statement to find the missing information related to empty fields in an application database.