Genetic Test Case Generation for Dynamic Product Coverage
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Solution Overview
Problem
Constructing test cases for large and complex products that optimally utilize resources while achieving desired test coverage is challenging due to the dynamic nature of product functions and the exponential growth of test points, leading to inefficiencies and ineffective test case design.
Innovation Solution
A method and system that uses genetic theory principles to automatically generate and update test cases by coding test elements as genetic sequences, allowing for random variations and natural selection to adapt test cases to product development, reducing overhead and ensuring dynamic test case evolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test cases are manually designed and updated to cover all product functions, then test coverage is improved, but the time and resources required increase exponentially
Solution Approach 1:
The system enables test cases to self-generate and self-update by automatically parsing product documentation, extracting test scenarios, and adapting to function changes without manual intervention. The test case generation system serves itself by autonomously maintaining test coverage as product functions evolve.
Solution Approach 2:
The system dynamically changes test case parameters by automatically updating test scenarios when product functions change. It monitors product documentation for modifications and automatically adjusts corresponding test cases, transforming static test cases into dynamic entities that adapt to parameter changes in the product.
2Reliability
If test cases are manually designed and updated to cover all product functions, then test coverage is improved, but the resource consumption increases
Solution Approach 1:
The system eliminates the need for manual test case creation and maintenance by implementing autonomous test case generation. It automatically parses product documentation, extracts test scenarios, and generates comprehensive test cases without human resource intervention, significantly improving resource efficiency.
Solution Approach 2:
The system replaces the mechanical process of manual test case design with an automated computational system. It uses software algorithms to parse documentation, extract test scenarios, and generate test cases, substituting human expertise with automated processing that is both efficient and scalable.
3Reliability
If test cases are manually updated with product function changes, then test case accuracy is improved, but the complexity of maintenance increases
Solution Approach 1:
The system implements a feedback mechanism that continuously monitors product documentation for changes. When documentation is updated, the system automatically detects the changes, identifies affected test cases, and updates them accordingly, creating a closed-loop system that maintains accuracy without increasing maintenance complexity.
Solution Approach 2:
The system performs preliminary actions by automatically parsing and analyzing product documentation before generating or updating test cases. It proactively identifies test scenarios and prepares test cases in advance, eliminating the need for reactive manual updates and reducing maintenance complexity.
Data Source
AI summary
Techniques for generating a test case involve: acquiring a first set of coding sequences representing a first set of test cases selected from a test case set for product testing. A test element in a test case of the test case set is coded based on the position of the test element in the element hierarchy of the test case set. The techniques further involve: generating a second set of coding sequences by performing a random variation related to at least one test element with respect to the first set of coding sequences; and generating a second set of test cases based on the second set of coding sequences. Accordingly, product testing and development may be automated, and the case set may change dynamically in a way that is adapted to product development while significantly reducing the overhead of designing and reviewing test cases throughout the product life cycle.