Automated Documentation Miner for Software Rule Extraction
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Solution Overview
Problem
Existing methods for identifying software rules and best practices in documentation are manual and inefficient, often leading to missed updates in code-generated specification files, especially when new functionality is launched or existing functionality is modified.
Innovation Solution
A system that programmatically mines documentation to automatically generate and publish rules and best practices, using a document miner and rule generator that retrieves documents, segments data, applies natural language processing, and classifies potential rules for incorporation into integrated development environment (IDE) plugins or code review tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to identify software rules in documentation, then human judgment and flexibility are maintained, but efficiency and completeness are poor leading to missed updates
Solution Approach 1:
The patent replaces manual mechanical reading and analysis of documentation with an automated computational system. The document miner and rule generator use NLP algorithms to automatically extract rules from documentation, eliminating the need for manual processing while maintaining comprehensive coverage and reducing missed updates.
Solution Approach 2:
The system enables self-service by automatically maintaining rule bases without requiring continuous manual intervention. The automated rule generator continuously monitors documentation changes and updates rules independently, allowing the system to serve itself in maintaining up-to-date rule specifications.
2Reliability
If code-generated specification files are used, then consistency and structure are improved, but they fail to capture new functionality or modifications timely
Solution Approach 1:
The system performs preliminary action by continuously monitoring documentation changes before they are reflected in code-generated files. The document miner detects changes in real-time and proactively updates rules, ensuring that specifications are maintained ahead of or同步 with code changes rather than reacting after the fact.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors documentation, extracts updated rules, and feeds them back into the specification files. This closed-loop approach ensures that any changes in documentation automatically trigger updates to generated specifications, maintaining reliability and timeliness.
3Quantity of substance
If comprehensive documentation is analyzed, then completeness of rules is improved, but processing time and complexity increase
Solution Approach 1:
The patent applies extraction by using NLP algorithms to selectively pull out only the relevant rule information from vast amounts of documentation. The system identifies and extracts specific patterns, constraints, and specifications that form rules, while filtering out irrelevant content, thereby achieving comprehensive rule coverage without processing every character of documentation.
Solution Approach 2:
The system segments the documentation processing into manageable units such as paragraphs, sentences, or structured sections. By dividing the large documentation corpus into smaller segments that can be processed independently through NLP algorithms, the system reduces processing time while maintaining comprehensive analysis of all documentation content.
Data Source
AI summary
Techniques for generating rules from documentation are described. For example, a method for generating rules may include generating one or more rules from documentation by: extracting a plurality of chunks from the documentation, inferring one or more candidate rules from the extracted chunks, mining the inferred one or more candidate rules to determine at least one of the one or more candidate rules is to be included in rule generation, classifying the at least one mined one or more candidate rules as one or more rules, and extracting information to generate the one or more rules.