LLM-Based Code Quality Evaluation and Iterative Improvement
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Solution Overview
Problem
Existing methods for evaluating and improving code quality are subjective, time-consuming, and inflexible, lacking comprehensive assessment across multiple dimensions and being specific to programming languages.
Innovation Solution
Utilizing a large language model (LLM) to evaluate and improve code quality by assessing multiple dimensions such as readability, maintainability, testability, efficiency, robustness, security, documentation, modularity, and scalability, with a framework that provides quantitative and qualitative feedback for iterative improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual code reviews are used to evaluate code quality, then the evaluation can be subjective and flexible, but it becomes time-consuming and requires programming language specific expertise
Solution Approach 1:
The patent replaces manual code review processes with an automated LLM-based evaluation system. The LLM processes code through API calls, automatically assessing multiple quality dimensions without human intervention, thereby eliminating the time-consuming nature of manual reviews while maintaining comprehensive evaluation capabilities
Solution Approach 2:
The LLM-based system provides universal evaluation across multiple programming languages and diverse code quality dimensions simultaneously. The same evaluation framework can assess readability, maintainability, testability, efficiency, robustness, security, documentation, modularity, and scalability across different programming languages without requiring language-specific expertise
2Productivity
If algorithmic assessments are used to evaluate code quality, then the evaluation becomes objective and efficient, but it becomes narrow focused, inflexible, and non-exhaustive
Solution Approach 1:
The system implements iterative feedback loops where the LLM evaluates code quality dimensions, provides detailed feedback reports, and enables code generation based on the evaluation results. This feedback mechanism allows the system to maintain objectivity while adapting to different code structures and providing comprehensive assessments across multiple quality dimensions
Solution Approach 2:
The patent changes the evaluation parameters from fixed algorithmic metrics to dynamic LLM-based assessments that can adapt to different programming languages and code styles. The system evaluates multiple quality dimensions including readability, maintainability, testability, efficiency, robustness, security, documentation, modularity, and scalability, providing flexible and comprehensive feedback
3Device complexity
If existing code quality evaluation methods are used, then the process is straightforward, but it lacks comprehensive assessment across multiple dimensions
Solution Approach 1:
The patent segments the code quality evaluation into multiple distinct dimensions including readability, maintainability, testability, efficiency, robustness, security, documentation, modularity, and scalability. Each dimension is evaluated separately by the LLM, allowing comprehensive assessment while maintaining an organized and manageable evaluation process through structured feedback reports
Data Source
AI summary
Automated evaluation and improvement of code quality is disclosed. A method may include: receiving code to optimize; evaluating the code for a plurality of code quality dimensions using a LLM, wherein the LLM returns a code quality score and a qualitative summary for each code quality dimension; generating an overall code quality score based on the code quality score and the qualitative summary; improving the code using the LLM based on the code quality score and the qualitative summary for each code quality dimension; evaluating the improved code for the plurality of code quality dimensions using the wherein the LLM returns an updated code quality score and an updated qualitative summary; generating an overall code quality score based on the updated code quality score and the updated qualitative summary; and outputting the improved code in response to the updated code quality score being higher than the overall code quality score.