Automated Document Regression Testing via Attribute Extraction
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Solution Overview
Problem
Automated regression testing of machine-generated documents is time-consuming and labor-intensive, requiring human analysis to ensure accuracy, especially after template changes, due to potential user or machine errors during data population and parsing.
Innovation Solution
A system comprising databases, processors, and memory that extracts attribute labels and values from multiple automatically generated documents, generates tabular reports, and alerts users to discrepancies, enabling bulk automated analysis and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated regression testing is performed on machine-generated documents, then productivity is improved, but measurement precision deteriorates due to potential user or machine errors during data population and parsing
Solution Approach 1:
The system automatically compares extracted data from generated documents against expected values and provides immediate feedback on discrepancies. This feedback mechanism enables continuous monitoring of data accuracy without requiring manual verification, thus maintaining both high productivity and measurement precision.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computer-based extraction and comparison systems. By using optical character recognition and automated data parsing, the system eliminates human error in verification while maintaining the ability to detect accuracy issues through systematic comparison against expected values.
2Measurement precision
If manual human analysis is used for regression testing, then measurement precision is improved, but loss of time increases due to time-consuming review processes
Solution Approach 1:
The system performs self-verification by automatically extracting data, comparing it against expected values, and generating reports without requiring continuous human intervention. This self-service capability maintains measurement precision through automated comparison while dramatically reducing the time investment required for regression testing.
Solution Approach 2:
The patent substitutes manual mechanical review with automated computer-based extraction and comparison systems. By using optical character recognition and programmed comparison logic, the system achieves both high measurement precision and reduced time consumption compared to manual analysis.
3Productivity
If bulk automated analysis is implemented, then productivity is improved, but device complexity increases due to multiple processing steps
Solution Approach 1:
The system divides the complex document analysis process into distinct segmented steps: data extraction, value comparison, and report generation. Each segment is handled by dedicated processing modules that work independently, enabling bulk automated analysis of thousands of documents while keeping the overall system structure manageable through modular design.
Data Source
AI summary
A system for confirming file integrity of automatically generated documents is disclosed. The system comprises one or more processors that execute instructions to receive a document template specifying one or more sections, each section comprising a set of labels for attributes and receive two or more automatically generated documents, each comprising the set of labels for attributes and values of each of those attributes. The system extracts the set of labels for attributes and values of each of those attributes from each of the two or more automatically generated documents. Finally, the system generates a tabular report comparing the values of each of the attributes and generates an alert for a human user if the value for any attribute in a first document is different from the value for that attribute in a second document.