ALM Artifact Structured Data Accuracy via NLP Analysis
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Solution Overview
Problem
ALM frameworks face challenges in maintaining accurate structured data, leading to low-quality reports due to missing or incorrect structured content in artifacts, which affects query and report accuracy.
Innovation Solution
A method utilizing natural language processing and machine learning to analyze metadata and unstructured content of ALM artifacts, identifying missing and inaccurate structured content, and providing data to correct or suggest changes, thereby enhancing the accuracy of traceability links and attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually maintain structured content in ALM artifacts, then data accuracy can be controlled, but user burden increases and productivity decreases
Solution Approach 1:
The system performs self-service by automatically analyzing unstructured content, identifying missing structured data elements, and suggesting corrections without requiring manual user intervention. The ALM application autonomously extracts structured content from unstructured artifact descriptions, maintaining data accuracy while freeing users from manual maintenance tasks.
2Ease of operation
If structured content is automatically extracted from unstructured content, then user burden is reduced, but accuracy of structured content may deteriorate
Solution Approach 1:
The system implements feedback by presenting extracted structured content suggestions to users for review and confirmation. Users can validate, correct, or reject the automatically extracted structured content, ensuring high accuracy while maintaining ease of operation. This feedback loop allows the system to learn from user corrections and improve future extractions.
3Loss of information
If comprehensive analysis of unstructured content is performed to identify missing structured content, then data completeness improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by analyzing unstructured content and identifying missing structured elements proactively, before they become problematic. By continuously monitoring and pre-processing artifact descriptions, the system ensures data completeness without requiring time-consuming manual analysis when reports are generated.
Data Source
AI summary
According to an embodiment of the present invention, an artifact is received, and unstructured content of the artifact is parsed and analyzed to identify data for one or more of missing structured content of the artifact and inaccurate structured content of the artifact. The identified data is then added to the artifact. Embodiments of the present invention can be used, for example, to provide data for missing and inaccurate structured content in artifacts of Application Lifecycle Management (ALM) frameworks, and improve accuracy of structured information that used to run queries and create reports.