Enterprise-Context Code Generation Using Historical Stories and Tests
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Solution Overview
Problem
Developing new features for enterprise applications is time- and resource-intensive, requiring technical expertise and domain knowledge due to the need to align with the enterprise's specific tools and libraries, and existing AI assistants lack the ability to customize code to the enterprise's existing codebase.
Innovation Solution
A software development system leveraging large language models (LLMs) and entity matching models to automatically generate code snippets contextualized to an enterprise's specifics by using historical code snippets and test cases, iteratively refined until all test cases pass, and integrating them into the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI assistants are used to generate code, then code generation speed is improved, but the code cannot be customized to the enterprise's existing codebase and context
Solution Approach 1:
The system performs preliminary actions by embedding and storing historical code snippets, test cases, and enterprise-specific context data before code generation requests. This pre-processing creates a contextualized knowledge base that the LLM can query during code generation, enabling both speed and customization. The historical stories and code patterns are prepared in advance and stored for rapid retrieval during the generation process.
Solution Approach 2:
The system introduces an intermediary layer between the LLM and the code generation process. This intermediary retrieves and injects enterprise-specific context, historical code patterns, and relevant test cases into the LLM's prompt. This mediator enables the LLM to generate customized code without sacrificing generation speed, as the contextual information is systematically provided through the intermediary retrieval mechanism.
2Adaptability or versatility
If manual code development is performed to ensure contextual alignment, then code quality and adaptability are improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system enables self-service by automatically retrieving relevant historical code snippets, test cases, and contextual information without human intervention. The entity matching model autonomously queries the stored embeddings, identifies relevant historical stories, and injects them into the LLM prompt. This self-service mechanism maintains high contextual alignment while eliminating the time-consuming manual research and code review processes.
Solution Approach 2:
The system implements feedback loops where generated code is automatically tested against retrieved test cases and historical patterns. The testing results feed back into the generation process, allowing iterative refinement. This automated feedback mechanism ensures contextual alignment without requiring manual code review, significantly reducing development time while maintaining quality.
3Reliability
If more historical code and test cases are retrieved to improve code quality, then code accuracy and reliability are improved, but system complexity and processing overhead increase
Solution Approach 1:
The system applies local quality by retrieving only the most relevant historical code snippets and test cases specific to the current generation context, rather than processing all available historical data. The entity matching model queries embeddings with specific parameters to retrieve a focused subset of relevant information. This selective retrieval maintains code accuracy while reducing processing complexity compared to comprehensive data processing.
Solution Approach 2:
The system changes parameters by adjusting the retrieval query parameters based on the specific code generation context. The entity matching model modifies search parameters dynamically to optimize the balance between retrieving sufficient relevant information and maintaining processing efficiency. This parameter optimization enables accurate code generation without excessive processing overhead.
Data Source
AI summary
Methods, systems, and computer-readable storage media for receiving a current story representative of a function that is to be added to an application, generating a current story embedding, determining a set of historical stories at least partially by comparing the current story embedding to historical story embeddings in a set of historical error embeddings, identifying a sub-set of historical stories from the set of historical stories, the sub-set of historical stories including one or more historical stories, retrieving a historical code snippet and a test case set associated with each historical story in the sub-set of historical stories, generating a code snippet for the current story using a large language model (LLM) system, and releasing the code snippet to a code repository for integration in the application.