Codebase-Specific Structure Configuration for Compliant Code Generation
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Solution Overview
Problem
Generating software products that comply with company standards and requirements is complex, requiring input from various designers and managers, complicating the development process.
Innovation Solution
A computerized method generates a structure configuration from a codebase using code examples and a standard configuration dataset, assigning labels to code components, and maps feature prompts to these components to automatically generate application code that complies with company standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual code generation with multiple designers and managers is used to ensure compliance with company standards, then compliance quality is improved, but device complexity and development time increase
Solution Approach 1:
The system enables self-service by automatically generating compliant code through ML models that internally enforce company standards, compliance rules, and theming requirements without requiring manual review from multiple designers and managers. The code generation platform autonomously ensures compliance while reducing development process complexity.
2Reliability
If manual code generation with multiple designers and managers is used to ensure compliance with company standards, then compliance quality is improved, but productivity decreases
Solution Approach 1:
The patent replaces the mechanical system of manual code review and approval by multiple designers and managers with an automated ML-based code generation platform. This substitution maintains compliance quality through programmed enforcement of company standards while dramatically improving productivity by eliminating iterative human review cycles.
3Productivity
If automatic code generation is used to improve productivity, then development efficiency is improved, but manufacturing precision may worsen due to potential compliance issues
Solution Approach 1:
The code generation platform incorporates feedback mechanisms where the ML model is trained on company standards, compliance rules, and theming requirements, and continuously refines code generation based on compliance validation. This feedback loop ensures that automatic code generation maintains high compliance accuracy while preserving development efficiency.
4Measurement precision
If a fine-tuned ML model is used to map feature prompts to code components, then measurement precision of feature translation is improved, but device complexity increases
Solution Approach 1:
The system applies preliminary action by pre-training and fine-tuning the ML model on company-specific codebases, compliance standards, and component libraries before deployment. This preliminary preparation enables the model to accurately map feature prompts to compliant code components without requiring complex runtime processing or additional validation layers.
Data Source
AI summary
Code examples of the codebase are obtained, and a structure configuration is generated using the obtained code examples and a standard configuration dataset, and includes code components specific to the codebase. Labels that are indicative of component attributes are assigned to the code components of the structure configuration. A code generation request is received, and the request is converted into a plurality of feature-prompts. At least one code component is mapped to each feature prompt based on semantic similarity of the feature prompt to a label of the mapped code component. Application code is generated using the mapped code components.