Test Data Mutation for DUT Validation
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Solution Overview
Problem
Conventional automated software testing methods produce test data randomly or based on test scripts, leading to low validity and incomplete testing, making it difficult to identify issues in the device under test (DUT) effectively.
Innovation Solution
A device and method for producing test data that involves storing simulated data conforming to the DUT's format, with a processor mutating these data in various forms to create diverse test data sets, allowing for complete and efficient testing by tracing errors to specific data blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If test data are produced randomly, then the productivity of test data generation is improved, but the validity of test data deteriorates
Solution Approach 1:
The patent pre-generates a large number of simulated test data in advance that conform to the DUT's data format, then uses these pre-generated data as basis for mutation testing. This preliminary action ensures both high productivity (automated generation) and high validity (conformance to format).
Solution Approach 2:
The patent creates copies of valid simulated test data and applies mutations to these copies. By copying valid data structures and modifying them systematically, the patent maintains the underlying validity while introducing controlled variations for comprehensive testing.
2Reliability
If test data are produced based on test scripts, then the validity of test data is improved, but the device complexity and time consumption increase
Solution Approach 1:
The patent makes the test data generation process self-service by automatically generating simulated test data that conform to the DUT's data format without requiring manual test script creation. The system autonomously produces valid test data structures.
Solution Approach 2:
The patent changes the approach from script-based generation to parameter-based mutation. By systematically varying parameters (mutation forms) applied to valid data structures, the patent simplifies the overall process while maintaining validity.
3Ease of operation
If conventional test data production methods are used, then the ease of operation is improved, but the measurement precision of error location deteriorates
Solution Approach 1:
The patent segments test data into multiple data blocks and applies different mutation forms to different segments. When errors occur, the segmentation allows precise identification of which specific data block caused the error, improving measurement precision while maintaining ease of operation through automated processes.
Solution Approach 2:
The patent introduces mutation forms as an intermediary mechanism between test data generation and error detection. This intermediary layer systematically tracks which mutations were applied, enabling precise error location while keeping the overall process simple and automated.
4Reliability
If comprehensive test coverage is achieved through multiple mutation forms, then the reliability of testing is improved, but the productivity of test data generation decreases
Solution Approach 1:
The patent applies different mutation forms in a periodic or systematic sequence to the simulated test data. This structured approach ensures comprehensive coverage of various mutation types while maintaining efficient automated generation, as the periodic application of mutations can be parallelized and automated.
Data Source
AI summary
A device for producing test data stores a plurality of simulated test data each of which substantially conforms to the data format accepted by a device under test (DUT). The data format includes different data blocks. The device for producing test data also mutates each of the simulated test data in one of a plurality of mutation forms to generate a plurality of first test data for testing the DUT. Each of the mutation forms refers to mutating one of the data blocks in one of a plurality of mutation ways.