Test Data Generation Using Tacit Knowledge Dictionary
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Solution Overview
Problem
Current techniques fail to appropriately generate test data for input value variation tests based on tacit knowledge, leading to unnecessary and ineffective tests due to manual effort and lack of consideration for input value constraints.
Innovation Solution
A test data generation device that utilizes a tacit knowledge test data dictionary to match input value constraints with generation conditions, generating test data only when constraints match, and using regular expressions to ensure data validity, thereby preventing unnecessary tests and ensuring intended test data is generated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test data is generated manually based on tacit knowledge, then test data quality improves, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-defining test data templates and constraints in a dictionary before actual test execution. This allows automated generation of high-quality test data without manual intervention during test runs, resolving the contradiction between quality and time consumption.
Solution Approach 2:
The system enables self-service by automatically generating test data based on predefined templates and constraints without requiring manual input. The automated generation process maintains quality standards while eliminating time-consuming manual operations.
2Reliability
If comprehensive screen transition tests are performed, then test coverage improves, but manual operations increase
Solution Approach 1:
The system replaces manual mechanical operations with automated script-based execution. Test scripts automatically navigate through screen transitions, eliminating the need for manual clicking and data input while maintaining comprehensive test coverage.
Solution Approach 2:
The system uses templates and patterns to generate and replicate test scenarios across multiple screen transitions. By copying proven test patterns, comprehensive coverage is achieved without proportionally increasing manual operation complexity.
3Productivity
If test data is generated without considering input value constraints, then generation speed improves, but test effectiveness decreases
Solution Approach 1:
The system dynamically adjusts test data generation parameters based on constraint specifications. By changing generation parameters to match constraints (such as data formats, ranges, and patterns), the system maintains both high generation speed and test effectiveness through automated constraint validation.
Data Source
AI summary
A test data generation device that generates test data to be input into an input field of a test target application includes a storage unit that stores a tacit knowledge test data dictionary including, for each tacit knowledge category, a generation method, test data information, and an input value constraint on an input field to be used as a generation condition, and a generation unit that refers to, regarding a certain tacit knowledge category for a target input field, the tacit knowledge test data dictionary to generate test data for the target input field when an input value constraint of the target input field in the test target application and an input value constraint of a generation condition regarding the tacit knowledge category are determined to match, and not to generate the test data for the target input field when the input value constraint of the target input field and the input value constraint of the generation condition regarding the tacit knowledge category are determined not to match.