Automated Software Component Reference Guide Generation

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Solution Overview

Problem

Developers face challenges in effectively implementing software components due to the vast number of available components and the complexity of integrating them, which can lead to performance degradation, business downtime, and security risks, as existing systems lack comprehensive documentation and natural language processing for quick understanding and troubleshooting.

Innovation Solution

A system and method for automatically generating a software component reference guide using multiple information sources, incorporating a Web GUI portal, Software Component Identifier, Source and Information Classifier, Component Guide Generator, Introduction Generation Service, Technology Guide Service, FAQ Generation Service, and Software Guide Natural Language Generator, employing machine learning and natural language processing to provide detailed documentation and FAQs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If developers manually review software component documentation from multiple sources, then they can understand the component details, but it takes considerable time and multiple trial and error efforts

Engineering Contradiction:

Improvecomponent information accessibilityVSAvoidtime to understand and implement component

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system automatically generates comprehensive documentation and FAQs by crawling and processing information from multiple sources without requiring manual intervention. The automated pipeline includes information gathering, processing, generation of documentation and FAQs, and delivery to developers, eliminating the time-consuming manual review process while maintaining complete information accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual information gathering and processing with automated computational systems. Machine learning models and natural language processing techniques automatically extract, process, and synthesize information from multiple sources, substituting the mechanical manual review process with intelligent automation that achieves both speed and completeness

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If developers integrate software components without comprehensive documentation, then integration speed increases, but performance degradation and security risks increase

Engineering Contradiction:

Improveintegration speedVSAvoidintegration reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary action by automatically generating comprehensive documentation and FAQs before the developer integrates the software component. The automated pipeline processes information from multiple sources, generates detailed guides, and delivers them in advance, ensuring that all necessary information is ready before integration begins, thus maintaining both speed and reliability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback mechanisms by analyzing developer questions and integrating that information into the generated documentation and FAQs. This iterative feedback loop ensures that the documentation addresses actual developer needs and concerns, improving both the relevance of information and the reliability of integration outcomes

Inventive Principle:

Principle #23Feedback

3Loss of information

If the system processes information from multiple sources, then documentation completeness improves, but system complexity increases

Engineering Contradiction:

Improvedocumentation completenessVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system segments the complex information processing task into distinct modular components: information gathering module, information processing module, documentation generation module, FAQ generation module, and delivery module. Each segment handles a specific aspect of the process independently, making the overall complex system manageable and maintainable while achieving complete documentation from multiple sources

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs universal processing components that can handle multiple types of information sources and generate multiple types of outputs. The machine learning models and NLP techniques serve multiple functions across different processing stages, reducing the need for specialized separate systems and thereby managing complexity while maintaining comprehensive documentation capability

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12106094B2Methods and systems for auto creation of software component reference guide from multiple information sources

Publication Date: 2024.10.01 OPEN WEAVER INC

AI summary

Systems and methods for automatically creating a software component reference guide from multiple information sources are disclosed. In one aspect, the method includes receiving, from a user, a request for reference guides for a software components and view corresponding results, identifying the software component, identifying different sources of information for the software component, generating introductory information of the software component, generating technology details of the software component, generating frequently asked questions (FAQs) and their related solutions associated with the software component, training a catalog of natural language terms related to the software components, and providing, based on the trained catalog, the introductory information, technology details, and FAQs to the user.