Automated Test Case Generation for Ambiguous Software Documentation
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Solution Overview
Problem
Existing software test case creation methods fail to capture exceptional behaviors that are not documented or are ambiguous, leading to incomplete testing and potential configuration issues, as these behaviors are often overlooked in system specifications and are not adequately addressed by current methodologies.
Innovation Solution
A method that extracts descriptions from document artifacts, categorizes them using a morphological analyzer and document categorizer, identifies keywords, particularly nouns with high frequency in ambiguous descriptions, and generates test cases to include these exceptional behaviors, ensuring comprehensive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test cases are created based on prior knowledge and system specifications, then test case creation is straightforward and efficient, but exceptional behaviors that are not documented are overlooked and not covered
Solution Approach 1:
The system performs self-analysis by automatically extracting exceptional behaviors from documentation and generating test cases without requiring manual identification by testers. The processor autonomously analyzes document artifacts, identifies ambiguous descriptions, and creates corresponding test cases, enabling the system to service itself rather than relying on human expertise to catch all edge cases.
Solution Approach 2:
The patent replaces the mechanical process of manual test case creation with an automated computational system. Instead of testers manually reviewing specifications and creating test cases based on their knowledge, a processor automatically extracts descriptions from document artifacts, analyzes them using morphological analyzers, and generates test cases programmatically, substituting human mechanical work with automated processing.
2Reliability
If all possible system behaviors are documented in specifications, then comprehensive test cases can be created, but documentation becomes excessively complex and difficult to maintain
Solution Approach 1:
The system changes the parameter of documentation from requiring complete explicit description to allowing implicit information. Instead of documenting every possible exceptional behavior explicitly (which would create ambiguity and complexity), the patent uses automated extraction and analysis to derive exceptional behaviors from existing documentation, changing the state of information representation from explicit to implicit while maintaining completeness.
Solution Approach 2:
The patent performs preliminary analysis of documentation to identify exceptional behaviors before actual test execution. By pre-processing the documentation artifacts and extracting ambiguous descriptions in advance, the system prepares test cases for exceptional behaviors ahead of time, rather than trying to identify them during test execution or relying on complete upfront documentation.
3Reliability
If manual review of all system documentation is performed to identify exceptional behaviors, then comprehensive test coverage is achieved, but time and resources are excessively consumed
Solution Approach 1:
The patent replaces manual review processes with automated computational analysis. Instead of human reviewers manually examining documentation to identify exceptional behaviors, a processor automatically extracts descriptions from document artifacts, uses morphological analyzers to identify ambiguous terms, and generates test cases programmatically, dramatically increasing productivity while maintaining detection capability.
Solution Approach 2:
The patent introduces an intermediary automated analysis system between the documentation and test case creation. This intermediary processor acts as a mediator that automatically extracts and analyzes information from documentation artifacts, identifying exceptional behaviors and generating test cases without requiring direct human intervention, thus bridging the gap between documentation and testing efficiently.
Data Source
AI summary
A test case generation method includes in a processor, extracting descriptions from document artifacts, extracting a first set of keywords from the descriptions, categorizing the descriptions to a first set and a second set, extracting a second set of keywords that occur in the second set and generating a test case from the second set of keywords.