Architecture Design Defect Detection Using Knowledge Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technology architecture design processes focus primarily on functional requirements, neglecting the evaluation of design structure, which leads to technical debt and suboptimal implementation, and lack automated tools for assessing and remediating design defects.
Innovation Solution
A method using machine learning applied to a knowledge graph to evaluate architecture designs, generating a new graph pattern, identifying design defects, and suggesting remedial changes through a behavior-driven architecture language in an integrated development environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tools are introduced to evaluate architecture design, then productivity and measurement precision are improved, but device complexity increases
Solution Approach 1:
The patent introduces an architecture knowledge graph as an intermediary layer between the architecture design document and the evaluation engine. The NLP engine processes the design document to generate a knowledge graph representation, which then serves as the basis for pattern matching and defect detection. This intermediary structure simplifies the overall evaluation process by standardizing the representation of architecture designs.
Solution Approach 2:
The system creates a graphical representation (copy) of the architecture design document in the form of an architecture knowledge graph. This graphical copy captures the essential structural relationships and components of the original design, allowing for automated analysis without directly manipulating the complex natural language document. The model graph patterns serve as templates for comparing against these copies.
2Manufacturing precision
If comprehensive architecture design evaluation is performed, then manufacturing precision and reliability are improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-defining model graph patterns that represent known architecture design defects and best practices. These patterns are established beforehand and stored in the knowledge graph. During evaluation, the system simply matches the architecture design against these pre-defined patterns, significantly reducing evaluation time while maintaining comprehensive defect detection capability.
Solution Approach 2:
The patent implements partial action by focusing the evaluation on specific graph pattern shapes and structural relationships rather than analyzing every detail of the architecture design document. The NLP engine extracts only the essential graphical representation elements needed for defect detection, performing a targeted rather than exhaustive analysis.
3Measurement precision
If manual review of architecture design is performed, then measurement precision is maintained, but productivity and ease of operation deteriorate
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computational system. The NLP engine automatically parses architecture design documents, the graph generation algorithm automatically creates knowledge graph representations, and the pattern matching algorithm automatically detects defects. This substitution maintains measurement precision through systematic rule-based evaluation while dramatically increasing productivity through automation.
Solution Approach 2:
The system enables self-service by allowing the architecture design evaluation to be performed automatically without requiring manual intervention from experts. The NLP engine and graph analysis algorithms autonomously process design documents, identify defects, and generate recommendations, freeing up human reviewers to focus on higher-level architectural decisions rather than detailed defect detection.
Data Source
AI summary
Systems and methods for identifying and remediating architecture design defects are disclosed. In one aspect, a method includes generating a new architecture graph pattern based on an architecture design document of an evaluated architecture; determining a model graph pattern, wherein a shape of the model graph pattern is similar to a shape of the architecture graph pattern; determining, based on a comparison of the shape of the model graph pattern with the shape of the new architecture graph pattern, that the new architecture graph pattern includes a design defect; generating, based on the shape of the model graph pattern, a remediated graph pattern; and determining, based on the differences between the remediated graph pattern and the new architecture graph pattern, a suggested remedial change to the architecture design document.