Distributed Processing Test Method Using Feature Pattern Matching
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Solution Overview
Problem
In distributed processing systems, the time required for testing a program's normal operation is prolonged when expected data cannot be generated promptly due to resource shortages, leading to inefficiencies in testing processes.
Innovation Solution
A method that generates a feature pattern from input data and compares it with stored patterns to determine if expected data needs to be calculated, allowing for immediate testing success determination if patterns match, thereby reducing the need for frequent expected data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expected data is generated separately for each unit of input data, then testing accuracy is maintained, but testing time increases due to resource shortages
Solution Approach 1:
The patent pre-calculates and stores feature patterns from input data before actual testing occurs. When testing, the system first generates a feature pattern from the input data and checks if it exists in the pre-stored patterns. If found, the corresponding expected data is immediately retrieved without regeneration, thus maintaining testing accuracy while significantly reducing testing time.
Solution Approach 2:
The system performs preliminary processing by extracting feature patterns from input data and storing them in advance. This preliminary action creates a lookup table of feature patterns and their corresponding expected data, allowing the testing phase to skip time-consuming data generation steps and directly compare results against pre-computed expectations.
2Productivity
If expected data is generated in advance, then testing speed improves, but resource requirements increase
Solution Approach 1:
The patent extracts only the essential feature patterns from input data rather than storing or processing the complete input datasets. By identifying and storing only the discriminatory feature patterns that determine expected outcomes, the system reduces resource requirements while maintaining the ability to quickly retrieve expected data during testing, thus improving testing speed without proportionally increasing resource consumption.
Data Source
AI summary
A program on a plurality of processing units executes test input data. In the case where an error occurs so that processing of the program is not completed normally, it is determined that a test performed by using the input data failed. Meanwhile, in the case where an error does not occur so that processing of the program is completed normally, if the same feature pattern as that of the input data is stored in a storing unit which stores feature patterns of the executed input data, it is determined that the test performed by using the input data succeeded, while if the feature pattern is not stored in the storing unit, the result of the test performed by using the input data is judged based on the result of comparing the expected data with result data of the program.