Automated Code Annotation with Dirichlet Smoothing for Lightweight Search
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Solution Overview
Problem
Existing codebases often lack documentation, making it difficult to understand and search through, and current neural network-based code documentation models require large codebases and expensive equipment for training.
Innovation Solution
A method for automated code analysis and tagging using a code annotation program that parses code snippets into data structures, quantifies keywords, and trains a probabilistic generative model based on Dirichlet prior smoothing estimates, enabling efficient annotation and search of codebases without specialized hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network-based code documentation models are used, then code annotation accuracy is improved, but hardware cost and training complexity increase
Solution Approach 1:
The patent replaces expensive, complex neural network models with a simpler, lightweight probabilistic generative model that can be trained on standard hardware without requiring GPUs or specialized equipment. The model uses basic probabilistic mathematics and Dirichlet prior smoothing estimates, making it accessible and disposable rather than requiring expensive infrastructure.
Solution Approach 2:
The patent substitutes the mechanical/neural network-based system with a mathematical probabilistic system. Instead of using neural networks that require complex hardware, the invention uses probabilistic generative models with Dirichlet priors and Gaussian kernel density estimates that can run on standard computers, replacing the mechanical computation of neural networks with mathematical probability theory.
2Measurement precision
If large codebases are used for training, then model performance is improved, but training time and computational resources increase
Solution Approach 1:
The patent changes the fundamental parameters of the training approach by using probabilistic mathematics instead of neural network parameters. The model uses Dirichlet prior smoothing estimates and Gaussian kernel bandwidth parameters that can be tuned without requiring large-scale training data. This allows the model to achieve good performance with smaller codebases and shorter training times while maintaining annotation accuracy.
3Quantity of substance
If codebases have no documentation, then codebase size is reduced, but code understanding and searchability decrease
Solution Approach 1:
The patent applies preliminary action by automatically generating documentation keywords and annotations for code snippets before they need to be searched or understood. The probabilistic model pre-processes the codebase, extracting structural features from Abstract Syntax Trees and generating relevant keywords that document the code's functionality, making the codebase searchable and understandable without manual documentation.
Solution Approach 2:
The patent implements self-service by enabling the codebase to annotate itself automatically. The probabilistic generative model analyzes the code structure and generates relevant keywords and documentation without human intervention. The code snippets serve their own documentation needs through automated keyword extraction based on structural features and training data, eliminating the need for external documentation efforts.
Data Source
AI summary
Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.