Enterprise Code Generation Using AI Retrieval and Coding Standards
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Solution Overview
Problem
Existing code generation systems lack the ability to produce enterprise-specific code that adheres to the unique standards and styles of a particular business organization, often leading to inconsistencies and inefficiencies in software development.
Innovation Solution
Utilizing generative AI models trained on a domain-specific codebase, these systems generate code that aligns with the enterprise's coding standards and style by considering surrounding context and leveraging retrieval models for accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing code generation systems are used, then code can be generated quickly, but the code does not adhere to enterprise-specific standards and styles
Solution Approach 1:
The system applies different quality standards to different parts of the code generation process. The base model generates code efficiently without constraints, while enterprise-specific guidelines are applied locally to refine and adjust specific aspects of the generated code to meet organizational standards, rather than applying rigid constraints throughout the entire generation process.
Solution Approach 2:
An intermediary layer is introduced between the code generation model and the final output. This intermediary consists of enterprise-specific guidelines and retrieval models that act as mediators, translating general code generation capabilities into enterprise-compliant code by applying organizational standards, coding conventions, and best practices without retraining the underlying model.
2Manufacturing precision
If code generation models are trained on enterprise-specific codebases, then coding standards adherence improves, but model retraining is required which consumes time and resources
Solution Approach 1:
Enterprise-specific guidelines and coding standards are prepared and stored in advance as retrievable knowledge bases before code generation is needed. This preliminary action allows the system to quickly retrieve and apply relevant standards during code generation without requiring time-consuming model retraining, thus maintaining both precision and efficiency.
Solution Approach 2:
Instead of retraining the model with enterprise-specific data, the system creates copies or representations of enterprise coding standards in the form of retrievable guidelines and rules. These copied standards can be quickly applied to generated code without modifying the underlying model, avoiding the time and computational resources required for retraining while maintaining adherence to organizational standards.
3Productivity
If generic code generation is used, then development speed is maintained, but code consistency across the enterprise deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where generated code is evaluated against enterprise-specific guidelines and standards. This feedback loop allows the system to maintain high development speed by using efficient base models while ensuring code consistency through automated checking and adjustment based on organizational standards, creating a balance between speed and uniformity.
Solution Approach 2:
The enterprise guideline system serves multiple functions simultaneously: it acts as a reference for code generation, a validation mechanism for consistency checking, and a source of best practices. This multi-functional approach allows the system to maintain development speed while ensuring code consistency across different teams and projects without requiring separate processes for each function.
Data Source
AI summary
Methods, systems, products, services, and apparatuses for generative AI systems configured to produce computer program code using a knowledge base that includes a client-specific code base, including: receiving one or more input tokens associated with a computer program; accessing information describing a domain-specific codebase; and generating, based on the one or more input tokens associated with the computer program and information describing a domain-specific codebase, suggested code to insert into the computer program.