Code Generation Quality Evaluation via Multidimensional Syntax Trees
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Solution Overview
Problem
Existing methods for evaluating automatic code generation quality and efficiency are limited as they focus on static results, ignoring dynamic interactions and code context changes, leading to low credibility and inability to effectively compare different models.
Innovation Solution
A method and apparatus that acquire interactive data sets between programmers and code generation tools, using multidimensional evaluation indicators to extract characteristic values, construct a multidimensional syntax tree, and perform feature learning with a convolutional neural network to generate evaluation values for code quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional evaluation indicators (Precision, Recall, MRR, F1-Measure) are used to evaluate automatic code generation, then the evaluation process is simple and straightforward, but the evaluation results lack credibility and cannot reflect dynamic interactions and code context changes
Solution Approach 1:
The patent segments the evaluation process into multiple independent modules: interactive data collection module, multidimensional indicator extraction module, syntax tree construction module, and neural network evaluation module. Each module handles a specific aspect of the evaluation, allowing the complex system to be managed through modular components while achieving comprehensive and credible evaluation results.
Solution Approach 2:
The patent introduces multidimensional evaluation indicators that add new dimensions to traditional evaluation. Instead of relying solely on single-dimension metrics like Precision and Recall, the system incorporates multiple dimensions including code quality, generation efficiency, and interactive behavior, thereby enhancing evaluation credibility through comprehensive multi-dimensional analysis.
2Measurement precision
If static evaluation methods are used to assess code generation results, then the evaluation process is fast and efficient, but the evaluation cannot capture dynamic interactions and code context changes
Solution Approach 1:
The patent performs preliminary actions by collecting interactive data and constructing syntax trees before the actual evaluation process. This preprocessing step organizes the data in advance, allowing the neural network to perform rapid evaluation on structured inputs, thereby reducing evaluation time while maintaining high measurement precision through comprehensive data analysis.
Solution Approach 2:
The patent creates a simplified copy of the code execution environment through syntax tree representations. Instead of running actual code executions to evaluate dynamic behavior, the system uses syntax trees as copies that capture the essential structural and contextual information, enabling fast evaluation without time-consuming actual executions while maintaining evaluation accuracy.
3Adaptability or versatility
If comprehensive multidimensional indicators are used to evaluate code generation, then the evaluation covers all aspects including dynamic interactions, but the data processing and analysis become extremely complex
Solution Approach 1:
The patent introduces syntax trees as an intermediary representation between raw interactive data and the neural network evaluation. The syntax tree acts as a mediator that structures complex multidimensional data into a standardized format, making it easier for the neural network to process and analyze comprehensive evaluation indicators without being overwhelmed by data complexity.
Solution Approach 2:
The patent replaces manual or rule-based data processing mechanisms with a neural network-based automated system. The neural network automatically learns patterns and relationships from the multidimensional indicators without requiring explicit programming of complex processing rules, thereby reducing the difficulty of detecting and measuring comprehensive evaluation criteria while maintaining high adaptability.
Data Source
AI summary
The disclosure discloses a method and an apparatus for determining code generation quality and efficiency evaluation values based on multiple indicators. The method includes that: an object code is acquired; an interactive data set is acquired; a multidimensional evaluation indicators is determined and characteristic values corresponding to each piece of data in the interactive data set are extracted; characteristic values corresponding to each piece of data in the interactive data set are determined as a characteristic data set; a multidimensional syntax tree is constructed; feature learning is performed on the multidimensional syntax tree to obtain feature parameters; the evaluation parameter model is controlled to output an evaluation value. According to the disclosure, the evaluation method in a related art is to evaluate a static automatic code generation result, ignoring the influence of code changes on the evaluation result, resulting in low reliability of the evaluation result is solved.