Code Recommendation Model Using Dependency Data
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Solution Overview
Problem
Existing code recommendation tools lack accuracy in predicting codes, as they rely solely on previous or current code segments without considering code dependence information, leading to inefficient development processes.
Innovation Solution
A method and apparatus for code recommendation that acquires relevant code segments and code dependence information to utilize a pre-trained code recommendation model, enhancing prediction accuracy by referencing both context and dependency data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If code recommendation tools rely solely on previous or current code segments for prediction, then the device complexity is reduced, but the manufacturing precision (code recommendation accuracy) deteriorates
Solution Approach 1:
The patent segments the code recommendation input into two distinct parts: relevant code segments (local context) and code dependence information (global dependencies). This segmentation allows the system to process different types of code information separately and feed them into the recommendation model, improving accuracy without overwhelming system complexity.
Solution Approach 2:
The patent adds a new dimension to code recommendation by incorporating code dependence information alongside traditional code segments. This transforms the recommendation from a single-dimensional approach (only code text) to a multi-dimensional approach that includes both code content and dependency relationships, significantly improving prediction accuracy.
2Loss of energy
If code recommendation tools use only current code segments, then the loss of information is minimized in terms of processing overhead, but the reliability of code prediction deteriorates
Solution Approach 1:
The patent performs preliminary action by extracting and organizing code dependence information before feeding it into the recommendation model. Dependencies such as imports, class hierarchies, and method calls are pre-processed and structured, allowing the model to efficiently utilize this information without excessive processing overhead during code generation.
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges the gap between raw code segments and the recommendation model. This intermediary extracts and structures code dependence information, transforming it into a format that the model can effectively utilize, thereby improving reliability without directly increasing processing overhead.
3Manufacturing precision
If code recommendation considers both relevant code segments and code dependence information, then the manufacturing precision (recommendation accuracy) is improved, but the device complexity increases
Solution Approach 1:
The patent merges relevant code segments and code dependence information into a unified input structure for the recommendation model. By combining these two types of information in a coordinated manner, the system achieves high recommendation accuracy while managing complexity through integrated processing rather than separate independent systems.
Solution Approach 2:
The patent creates a universal code recommendation framework that can handle both code segments and dependence information through a single multi-functional model. This universal approach improves accuracy by considering multiple factors simultaneously while avoiding the complexity of multiple specialized systems.
Data Source
AI summary
There is provided a code recommendation method and apparatus, an electronic device and a storage medium, which relates to the field of software development technologies, artificial intelligence technologies, or the like. A specific implementation includes: acquiring a relevant code segment required by a recommended code from a current code file; acquiring code dependence information required by the recommended code; and performing code recommendation based on the relevant code segment, the code dependence information and a pre-trained code recommendation model.