Context Engine Code Testing for Secure Intent-Based Generation
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Solution Overview
Problem
Existing code generation systems are insecure, inefficient, and inflexible, generating hazardous or harmful code without contextual testing, consuming excessive computational resources and failing to adapt to new test cases.
Innovation Solution
A test generation system utilizing a context engine with a large language model generates function and test code in tandem, ensuring contextual relevance and extensive testing through a context engine integrated with a large language model, including fine-tuning to generate test code from an intent query and executing it to validate function code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If code is tested on dedicated testing devices before deployment, then security and stability are improved, but time consumption and computational cost increase significantly
Solution Approach 1:
The system generates test cases and executes them automatically as part of the code generation process itself, before deployment. The context engine creates contextual test cases based on the generated code's functionality, performing security and correctness checks in advance without requiring separate dedicated testing phases.
Solution Approach 2:
The code generation system performs its own testing through the context engine, which automatically generates and executes test cases for the generated code. This self-testing mechanism eliminates the need for external dedicated testing devices and manual testing processes, significantly reducing time consumption while maintaining security verification.
2Measurement precision
If manual test code is written by engineers for each test case, then testing accuracy is improved, but device complexity and operational overhead increase
Solution Approach 1:
The context engine automatically generates test cases and test code based on the generated code's functionality and context. This automated test generation eliminates the need for manual test code writing by engineers, reducing operational overhead while maintaining high testing accuracy through context-aware test case creation.
Solution Approach 2:
The system changes the approach from manual test case parameters to automated parameter generation based on code analysis. The context engine extracts functional parameters and test conditions directly from the generated code, automatically creating appropriate test cases without requiring manual specification of test parameters by engineers.
3Manufacturing precision
If extensive testing is performed across multiple test cases, then code accuracy is improved, but computational resources and processing power are excessively consumed
Solution Approach 1:
The context engine performs targeted testing by generating test cases that are specifically relevant to the generated code's functionality. By performing preliminary analysis of the code to identify critical test paths and edge cases, the system achieves comprehensive testing coverage without exhaustively testing all possible scenarios, thereby reducing computational energy consumption while maintaining high code accuracy.
4Productivity
If code generation is performed without contextual testing data, then generation speed is improved, but security risks and harmful code output increase
Solution Approach 1:
The context engine performs preliminary contextual analysis and generates appropriate test cases immediately after code generation. This preliminary testing phase verifies security and correctness before deployment, eliminating security risks without requiring separate post-generation testing phases that would slow down the overall process.
Solution Approach 2:
The system implements feedback loops where test results from the context engine are used to verify and potentially refine the generated code. This feedback mechanism ensures security and correctness by automatically detecting and reporting issues in generated code, maintaining high generation speeds while eliminating security risks through immediate verification.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.