NLP Test Data Generation via Structural Pattern Extraction
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Solution Overview
Problem
In software testing, insufficient test data and lack of access to requirements hinder thorough testing, leading to difficulties in detecting patterns and generating sufficient samples, which can result in errors and resource inefficiencies.
Innovation Solution
A device extracts structural unit values from textual content samples to identify patterns and generate additional samples based on these patterns, automating the process to reduce manual effort and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual pattern identification and sample generation is performed, then testing thoroughness can be improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The system automatically identifies patterns from input samples and generates additional test samples without human intervention. The computer program autonomously performs pattern recognition and sample generation tasks that would otherwise require manual testing effort, thereby reducing time consumption while maintaining testing thoroughness.
Solution Approach 2:
Manual pattern identification and sample generation activities are replaced by an automated computer program. The mechanical process of manual testing is substituted with an electronic computing system that can rapidly analyze samples, identify patterns, and generate additional test data, significantly reducing time consumption.
2Reliability
If more test samples are generated manually, then testing coverage improves, but resource efficiency deteriorates
Solution Approach 1:
The system autonomously generates additional test samples based on identified patterns from the input samples. This self-service capability eliminates the need for manual sample generation, improving resource efficiency by automating the process while simultaneously expanding testing coverage through pattern-based replication of valid and invalid samples.
Solution Approach 2:
The system creates additional test samples by copying and transforming patterns identified from the original input samples. Instead of manually creating new samples, the program replicates the structural patterns of valid and invalid samples, generating numerous test cases efficiently while maintaining testing coverage.
3Measurement precision
If pattern identification is performed manually, then accuracy can be maintained, but the complexity of the testing process increases
Solution Approach 1:
The system extracts patterns from input samples by isolating and analyzing structural characteristics automatically. This extraction process identifies recurring patterns in valid and invalid samples without requiring manual analysis, maintaining accuracy while reducing process complexity by consolidating multiple manual steps into an automated extraction routine.
Solution Approach 2:
Manual pattern identification is replaced by an automated computer program that systematically analyzes samples to identify patterns. This substitution reduces testing process complexity by replacing complex manual analytical steps with a standardized computational process that maintains identification accuracy.
Data Source
AI summary
A method may include receiving a plurality of samples that include textual content. The method may include extracting unit values, corresponding to structural units, from the plurality of samples. The structural units may identify characteristics of the plurality of samples to be used to identify pattern information. The pattern information may identify unit values that are shared between at least two samples of the plurality of samples. The method may include generating one or more structural representations based on the unit values. The one or more structural representations may identify the pattern information. The method may include generating one or more additional samples based on the one or more structural representations. The one or more additional samples may include at least one of the unit values, and may be generated based on the pattern information. The method may include outputting the one or more additional samples.