Source Code Documentation via Machine Learning Metadata

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Solution Overview

Problem

Existing computer systems face inefficiencies in analyzing and interpreting source code due to dynamic programming implementations, leading to resource consumption and reduced throughput, and struggle to generate accurate documentation from source code.

Innovation Solution

A system utilizing machine learning and analysis techniques to interpret and generate documentation for source code by analyzing metadata, creating a source code model, and converting it into a graphical representation, thereby improving resource management and efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional code analysis methods are used to interpret source code, then the system can analyze code structure, but processing resources are heavily consumed and throughput is reduced

Engineering Contradiction:

Improvecode interpretation accuracyVSAvoidsystem throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs preliminary analysis by generating metadata from source code before full interpretation is needed. This metadata contains pre-extracted information about operations, variables, and control flow that can be quickly referenced during code analysis, reducing the need for repeated heavy processing and improving throughput while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical code parsing methods with machine learning models that have been trained to recognize code patterns. The ML models learn from metadata and can interpret code operations more efficiently than rule-based systems, reducing processing resource consumption while maintaining or improving interpretation accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If dynamic programming implementations are accommodated, then code flexibility is maintained, but interpretation difficulty increases

Engineering Contradiction:

Improveprogramming flexibilityVSAvoidcode interpretation difficulty

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system generates metadata that serves itself by capturing essential code characteristics in a structured format. This metadata automatically documents the dynamic behavior and operations, making the code self-describing and easier to interpret without requiring deep analysis of the dynamic implementation details.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent introduces metadata as an intermediary layer between the source code and the analysis system. This metadata acts as a bridge that translates complex dynamic programming constructs into simplified representations that are easier to interpret, reducing the difficulty of analyzing flexible code while preserving its adaptability.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If manual documentation generation is performed, then documentation accuracy is high, but time consumption and resource usage increase

Engineering Contradiction:

Improvedocumentation accuracyVSAvoiddocumentation generation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically generates documentation by copying and transforming information from the source code and its associated metadata. The metadata contains structured information about code operations and variables that can be directly translated into documentation, creating accurate documentation automatically without manual writing while maintaining high accuracy through the structured metadata source.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs preliminary extraction of documentation-relevant information during the metadata generation phase. By capturing operation details, variable definitions, and control flow information in the metadata beforehand, the actual documentation generation becomes a quick transformation process rather than requiring time-consuming manual analysis and writing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11314503B2Software development documentation using machine learning

Publication Date: 2022.04.26 BANK OF AMERICA CORP

AI summary

A device configured to receive source code for an application and analyze the source code to generate metadata for the source code. The metadata identifies operations performed within the source code. The device is further configured to generate a source code model for the source code based on the metadata for the source code and source code modeling instructions. The source code modeling instructions are configured to map the metadata associated with the source code to fields of the source code model. The device is further configured to convert the source code model into a graphical representation using visualization instructions. The visualization instructions include instructions for converting the source code model into the graphical representation. The device is further configured to output the graphical representation.