AI Referential Tagging for Automated Test Case Validation
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Solution Overview
Problem
System testing in complex environments is challenging due to the interdependence of numerous hardware and software components, making it difficult to identify and execute relevant regression tests efficiently, as existing methods rely heavily on manual selection by experienced engineers, which can be time-consuming and costly.
Innovation Solution
The implementation of referential artificial intelligence functionality that automatically generates and validates tags for test cases using machine learning and natural language processing, allowing for intelligent automated testing and targeted selection of relevant test cases based on changes in the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of regression tests by experienced engineers is used, then testing accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service by automatically generating regression test cases through AI analysis of code changes, eliminating the need for manual test selection by engineers. The AI model autonomously identifies affected components and generates relevant test cases, allowing the system to serve itself without human intervention in the test selection process.
Solution Approach 2:
The patent replaces the mechanical system of manual test case selection with an automated AI-based system. The artificial intelligence model analyzes code changes, identifies affected components, and generates regression test cases automatically, substituting the human engineer's manual process with an automated computational approach that maintains accuracy while reducing time consumption.
2Reliability
If all test cases are executed for comprehensive system testing, then system reliability is improved, but productivity decreases due to the large number of tests required
Solution Approach 1:
The system extracts only the necessary regression test cases from the complete test suite by analyzing code changes and identifying affected components. Instead of executing all test cases, the AI model extracts and generates only those tests that are relevant to the current changes, maintaining system reliability while improving productivity by reducing the number of tests that need to be executed.
Solution Approach 2:
The patent applies partial action by executing only a subset of test cases that are relevant to the current code changes rather than running the entire test suite. The AI model determines the appropriate scope of testing based on change analysis, performing just enough testing to ensure reliability without the excessive action of running all possible tests, thereby improving productivity while maintaining quality.
3Ease of operation
If intuitive tagging by test engineers is used, then test case organization is achieved, but the process becomes time-consuming and does not scale well
Solution Approach 1:
The system implements self-service by automatically generating tags for test cases through AI analysis. The AI model extracts meaningful tags from code changes and test case content without requiring manual intervention from test engineers. This automated tagging process maintains ease of operation for organizing test cases while dramatically improving productivity by eliminating the time-consuming manual tagging process.
Solution Approach 2:
The patent replaces the manual mechanical process of intuitive tagging with an automated AI-based tagging system. The artificial intelligence model analyzes test case content, code changes, and component relationships to generate appropriate tags automatically, substituting the human engineer's manual tagging process with an automated system that scales efficiently while maintaining or improving organization quality.
Data Source
AI summary
Generating referential artificial intelligence functionality for intuitively tagging infrastructure may include: generating, automatically, a set of tags based on a collection of test cases; tagging a test case with one or more automatically generated tags from the set of tags; running the test case on a system-under-test (SUT); determining that a result of the testing identifies a fault related to a first tag of the one or more automatically generated tags of the test case; and validating an association between the first tag and the test case in response to identifying that the fault is related to the first tag.