Software Package Categorization via Unique Attribute Extraction
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Solution Overview
Problem
Existing techniques are limited in categorizing and organizing software packages effectively, making it challenging to measure security and compliance risks, identify unique components, and optimize storage, due to the unstructured nature of software packages in repositories.
Innovation Solution
Determining unique attributes of software packages through package manager metadata, version and release metadata, and content signatures, and using these attributes to train machine learning models for accurate categorization and organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If software packages are stored in an unorganized state in repositories, then storage capacity can be maximized, but security and compliance risk assessment becomes difficult
Solution Approach 1:
The patent segments software packages into organized groups based on extracted attributes such as package name, version, license type, and dependencies. This segmentation transforms the unorganized mass of packages into structured categories, enabling both efficient storage utilization and reliable security assessment through organized access and analysis.
Solution Approach 2:
The patent changes the organizational parameters of software packages by extracting and analyzing multiple attributes (package metadata, version information, license types, dependency relationships). These parameter changes enable the system to organize packages along multiple dimensions simultaneously, achieving both storage efficiency and security assessability.
2Ease of manufacture
If traditional categorization methods are used for software packages, then the process is simple, but accuracy in identifying unique components and measuring vulnerability information is insufficient
Solution Approach 1:
The patent adds multiple dimensional attributes to software package categorization beyond traditional single-criterion methods. By analyzing package metadata, version information, license types, and dependency graphs simultaneously, the system creates a multi-dimensional classification framework that maintains procedural simplicity while dramatically improving measurement precision for vulnerability assessment.
Solution Approach 2:
The patent creates a composite categorization approach that combines multiple attribute types (metadata attributes, version attributes, license attributes, dependency attributes) into an integrated classification system. This composite method preserves the simplicity of automated processing while achieving high precision in identifying unique components and vulnerability information through the synergistic combination of multiple classification criteria.
3Measurement precision
If software packages are analyzed in detail to identify unique attributes, then categorization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary extraction and analysis of software package attributes during the initial organization phase. By pre-processing and storing key attributes (package metadata, version information, license types, dependency relationships) in an organized structure, the system eliminates the need for repeated detailed analysis during security assessments, thereby maintaining high categorization accuracy while significantly reducing processing time for subsequent operations.
4Measurement precision
If comprehensive attribute extraction is performed on all software packages, then unique identification accuracy improves, but storage requirements for metadata increase
Solution Approach 1:
The patent extracts only the essential and most discriminative attributes from software packages for organization and analysis. By selectively extracting key metadata (package name, version, license type, dependency relationships) rather than storing all possible package information, the system achieves high unique identification accuracy while minimizing metadata storage requirements. The extraction process focuses on attributes that provide maximum discriminatory power for categorization.
Data Source
AI summary
A set of attributes of software packages may be determined by analyzing a first set of software packages, where the set of attributes of software packages may be useful for uniquely identifying software packages in the first set of software packages. A heuristic may be created or a machine learning model may be trained that combines the set of attributes of software packages to uniquely identify software packages in the first set of software packages. The heuristic or the trained machine learning model may be used to categorize a second set of software packages, or determine relationships among a second set of software packages.