Automated Negative Test Input Data Generation from Requirements
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack an efficient way to generate negative test input data for applications, particularly for string, numeric, or character attributes, from requirements specifications models, which is essential for testing fault tolerance and exception handling.
Innovation Solution
A method and system that extract attributes and their properties from requirements specifications, convert them into constraint representation syntax, model structured diagrams, construct path predicates, and use these to generate negative test input data for attribute classes, employing techniques like genetic algorithms for string attributes and boundary/value analysis for numeric attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing methods are used to generate negative test input data, then comprehensive exception handling can be verified, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary extraction of attributes and their properties from requirements specifications before actual test data generation. By pre-processing the requirements model to identify all relevant attributes, constraints, and relationships, the system prepares negative test input data generation in advance, significantly reducing the time required during actual testing while maintaining comprehensive exception handling verification
Solution Approach 2:
The system automatically generates negative test input data by itself without requiring manual tester intervention. It self-services by extracting attributes from requirements, determining attribute classes, and generating appropriate negative test data through automated processes, thereby eliminating manual effort while ensuring thorough exception handling coverage
2Productivity
If automated test data generation is implemented, then testing efficiency improves, but techniques for generating negative test input data for string attributes are currently unavailable
Solution Approach 1:
The system implements a universal attribute class determination mechanism that handles multiple attribute types (string, numeric, character) through a unified framework. By creating a multi-functional attribute class determination process that adapts to different data types, the system achieves both high testing efficiency through automation and comprehensive adaptability across all attribute types including string attributes
3Reliability
If comprehensive negative test input data is generated for all attribute types, then functional coverage improves, but the complexity of the generation process increases
Solution Approach 1:
The system segments the complex test data generation process into distinct, manageable components: attribute extraction from requirements, attribute class determination, constraint analysis, and negative test data generation. By dividing the overall process into these discrete segments, the system maintains high functional coverage while reducing the perceived and actual complexity through modularization and structured processing
Data Source
AI summary
The present invention provides a method and system for generating negative test input data. A set of attributes and a set of attribute properties can be extracted from a requirement specification. A constraint representation syntax can be framed from the extracted set of attribute properties. A structured diagram is modeled from the framed constraint representation syntax and a set of use cases, a set of path predicates can be constructed from the structured diagram. One or more attribute classes can be determined from the set of path predicates based on an attribute constraint and an attribute dependency. The negative test input data shall be generated from the one or more attribute classes using genetic algorithm.