LLM Requirement Validation for Real-Time Compliance Feedback
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Solution Overview
Problem
Existing systems struggle with accurately interpreting and validating software requirements due to language variability, leading to ambiguities and non-compliance with standards, particularly in safety-critical domains, and lack scalability and adaptability to changing standards.
Innovation Solution
Implementing language models, such as large language models (LLMs) with prompt tuning, to process and refine software requirements, providing real-time feedback on clarity, compliance, and adherence to industry standards through natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems process software requirements, then they can identify ambiguities and non-compliance issues, but they require significant computational resources and cannot provide real-time feedback
Solution Approach 1:
The patent replaces conventional mechanical processing systems with neural networks that use natural language processing to interpret and validate software requirements. This substitution enables more accurate and efficient processing with reduced computational resource requirements compared to traditional systems.
Solution Approach 2:
The patent changes the operational parameters by using neural networks trained on extensive data to process requirements. This parameter change in the processing methodology enables real-time feedback generation while maintaining high accuracy in identifying ambiguities and non-compliance issues.
2Reliability
If conventional systems process software requirements, then they can validate against standards, but they lack scalability and adaptability when standards change
Solution Approach 1:
The patent implements a dynamic system where neural networks can be retrained or adjusted when software standards change. This dynamic adaptability allows the system to maintain compliance validation accuracy while adapting to new standards, unlike conventional static systems.
Solution Approach 2:
The system changes its operational parameters by updating neural network models when standards evolve. This parameter change enables the system to maintain high compliance validation reliability while achieving scalability and adaptability to changing industry standards.
3Measurement precision
If detailed validation of software requirements is performed, then accuracy and compliance can be improved, but processing time and efficiency are reduced
Solution Approach 1:
The patent substitutes detailed manual validation processes with neural network-based automated validation. This substitution maintains measurement precision in terms of requirement clarity while dramatically improving processing efficiency and productivity.
Solution Approach 2:
The neural network system performs self-validation by automatically detecting ambiguities and non-compliance issues without requiring extensive manual review. This self-service capability maintains high measurement precision while significantly improving processing efficiency.
Data Source
AI summary
Various examples, systems, and methods are disclosed relating to a computer system that can be designed for software development. The computer system can identify or access written details about the requirements for a software product. Using these requirements, the computer system can generate prompts that guide the operation of the software. The computer system can use the prompts and the initial requirements to produce feedback through a neural network, such as a large language model. The neural network can be trained with examples of software requirements and corresponding feedback. The feedback can suggest changes or confirm the requirements. Additionally, the computer system can provide the feedback, used for refining and improving software requirements.