Source Code Color-Coding for AI Training Efficiency
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Solution Overview
Problem
The evaluation of source code by human reviewers is inefficient due to the need for visual inspection, which can be time-consuming and burdensome, especially when artificial intelligence is trained using large images of source code snippets as training data, leading to prolonged learning times.
Innovation Solution
An information processing apparatus that divides source code into smaller sections, converts characters into colors based on predefined rules, and uses neural networks to learn and evaluate source code quality, reducing the load on human reviewers and accelerating the training process by generating and processing smaller, more manageable image-based training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If source code is evaluated by visual inspection by human reviewers, then evaluation can be performed with human judgment, but the evaluation process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the mechanical visual inspection process with an automated image processing system. Source code is converted into color-coded images where different syntax elements are represented by different colors, allowing machine-based evaluation instead of human visual inspection. This substitution maintains evaluation accuracy while dramatically reducing the time required.
Solution Approach 2:
The patent applies color coding to different parts of the source code syntax (keywords, identifiers, operators, etc.) to create visually distinct image representations. This color transformation enables automated systems to quickly identify and evaluate code structures without requiring human reviewers to parse text, thus reducing evaluation time while maintaining precision through systematic color-based pattern recognition.
2Reliability
If large images of source code snippets are used as training data for artificial intelligence, then comprehensive code patterns can be learned, but the learning time becomes prolonged
Solution Approach 1:
The patent divides source code into smaller semantic units (statements, expressions, or syntactic elements) and converts each segment into a separate color-coded image. This segmentation allows AI to learn from multiple smaller, focused examples rather than processing few large images, improving training quality while reducing the time required to process each training sample.
Solution Approach 2:
The patent transforms source code from text format into color-coded image format, changing the representation parameters. This transformation enables AI models to process code as visual patterns rather than text, allowing for more efficient learning of code structures and patterns while maintaining the comprehensive information needed for high-quality training.
Data Source
AI summary
An information processing apparatus includes a memory and a processor coupled to the memory. The processor is configured to obtain a source code. The processor is configured to generate color information by executing conversion processing with regard to the source code while following a rule for converting a character into a color or converting a color of a character in accordance with a type of the character. The processor is configured to output the generated color information.