Language Model Analysis of Software Architecture Diagrams
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Solution Overview
Problem
Analyzing software architecture documents requires significant time and effort, and is prone to user error due to their large size and complexity, making it difficult to efficiently verify correctness and identify security weaknesses.
Innovation Solution
Utilizing language models to parse and analyze architectural information, generating relational diagrams, and performing verification tasks with minimal user input, focusing on relevant portions of the data to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually review software architecture documents using checklists, then verification thoroughness is improved, but analysis time and user effort increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a parser and language model that mediates between the software architecture documents and the verification process. The parser extracts architectural information and generates relational diagrams, while the language model processes these diagrams to identify security weaknesses and verify correctness, thereby reducing direct user involvement while maintaining verification quality
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. Instead of users manually checking architecture documents against checklists, the system uses language models to process architectural information, generate relational diagrams, and automatically identify security issues, substituting human mechanical review with automated AI-based analysis
2Measurement precision
If users manually analyze large software architecture documents, then accuracy is maintained through human judgment, but user error increases due to document size and complexity
Solution Approach 1:
The patent segments the large software architecture documents into manageable components by parsing them into structured architectural information and relational diagrams. This segmentation allows the language model to process specific architectural elements systematically, reducing the cognitive load and error rate associated with analyzing large, unstructured documents manually
Solution Approach 2:
The parser and language model act as intermediaries that systematically process architectural information, eliminating human judgment errors. The system objectively analyzes architectural relationships and identifies security weaknesses based on learned patterns, providing consistent and reliable results without the variability and error-proneness of manual human review
3Reliability
If all architectural information is processed for analysis, then comprehensive verification is achieved, but processing time increases due to irrelevant portions
Solution Approach 1:
The patent extracts only the relevant architectural information needed for verification by parsing documents and generating relational diagrams that focus on security-critical relationships. The language model then processes these extracted relational structures to identify weaknesses, eliminating the need to process irrelevant portions of the original documents while maintaining verification completeness
Solution Approach 2:
The patent applies local quality by directing detailed analysis only to relevant architectural components identified through the relational diagrams. Instead of uniformly processing all architectural information, the system focuses computational resources on specific architectural elements that are most relevant to security verification, improving overall processing efficiency
Data Source
AI summary
In various examples, analyzing software architectural information using language models is described herein. Systems and methods are disclosed that parse architectural information associated with software—such as software architecture documents (SWADs), design documents, and/or source code—to generate relational diagrams associated with the architectural information. The systems and methods may then use the relational diagrams and one or more language models to analyze the architectural information. For instance, one or more prompts associated with analyzing the architectural information may be obtained, where an individual prompt is associated with performing one or more analysis tasks. The language model(s) may then process input data representing the prompt(s) along with at least a portion of the architectural information (e.g., determined using the relational diagrams) to determine information associated with the tasks.