Semantic Bridge for Automatic Trace Retrieval
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Solution Overview
Problem
Ensuring traceability between artifacts from different models is challenging due to differing terminologies and perspectives, making automated techniques ineffective in determining correspondences between distinct but related models.
Innovation Solution
A method for automatic trace retrieval using a semantic transformation table generated from training data, employing computer learning techniques and association rule mining to convert between different semantic styles, enabling tracing of associations between artifacts from regulatory and software programming environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated trace retrieval techniques are used between different models, then productivity is improved, but measurement precision deteriorates due to differing terminologies and perspectives
Solution Approach 1:
The patent introduces a semantic transformation table as an intermediary component that mediates between artifacts from different models. This table stores correspondence relationships between artifacts with different terminologies and perspectives, enabling automated trace retrieval while maintaining accuracy by translating between different semantic styles during the tracing process
Solution Approach 2:
The patent transforms the semantic parameters of artifacts by converting them through the semantic transformation table. This parameter change allows artifacts expressed in different semantic styles (e.g., regulatory terminology vs. software engineering terminology) to be matched accurately, resolving the contradiction between automated efficiency and retrieval precision
2Measurement precision
If manual traceability methods are used to ensure accuracy between different models, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing the semantic transformation table with correspondence relationships between artifacts from different models before actual trace retrieval operations. This preliminary setup enables subsequent automated tracing to achieve both high accuracy and efficiency without requiring manual intervention during the retrieval process
Solution Approach 2:
The patent creates a copied representation of artifact relationships in the semantic transformation table, which stores the correspondence patterns learned from manual tracing. This copied knowledge base enables automated systems to replicate manual tracing accuracy at machine speed, eliminating the time loss associated with manual methods
Data Source
AI summary
A method for performing automatic trace retrieval includes receiving a first and second model for a system or service (S10). The first model includes a first plurality of model artifacts at least partially represented by a first semantic style and the second model includes a second plurality of artifacts at least partially represented by a second semantic style. Training data including one or more correspondences between artifacts of the first plurality of model artifacts and artifacts of the second plurality of artifacts is collected. A semantic transformation table is generated for converting between the first and second semantic styles using the collected training data (S11). The generated semantic transformation table is used in tracing associations between artifacts of the first plurality of artifacts and artifacts of the second plurality of artifacts (S12).