Software Feature Vector Database for Compatibility Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software projects with hundreds or thousands of features make it impractical for developers to compare and determine compatibility with computing environments, as many features are internally defined and not explicitly described.
Innovation Solution
A system automatically builds a searchable database of software features by analyzing descriptive information from various sources, generating feature vectors, and storing them for easy comparison and searching, using techniques like keyword parsing, count techniques, and machine-learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If software projects store hundreds or thousands of features, then the software project information becomes comprehensive and detailed, but it becomes impractical for developers to compare and determine compatibility
Solution Approach 1:
The patent segments the large set of software features into structured feature vectors with defined categories and hierarchies. By organizing features into groups and subgroups with systematic naming conventions, the system transforms an overwhelming collection of individual features into a manageable structured format that enables efficient comparison while preserving comprehensive feature information.
Solution Approach 2:
The patent transforms software feature data from unstructured text descriptions into standardized parameter-based feature vectors. By converting feature information into consistent numerical or categorical parameters with uniform data types and scales, the system enables automated comparison and compatibility determination across diverse software projects while maintaining detailed feature representations.
2Loss of information
If many software features are internally defined and not explicitly described, then the software project functionality is comprehensive, but it becomes difficult to determine compatibility with computing environments
Solution Approach 1:
The patent performs preliminary extraction and standardization of software features from source code, documentation, and configuration files before compatibility assessment. By pre-processing feature information into structured feature vectors with consistent formats and explicit descriptions, the system makes internally defined features accessible and comparable, enabling reliable compatibility determination without requiring manual documentation of all features.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between internal software feature definitions and compatibility assessment requirements. These feature vectors serve as a standardized interface that translates diverse internal feature representations into a common format, enabling accurate compatibility determination even when original feature descriptions are minimal or internally defined.
3Measurement precision
If developers manually analyze software features for comparison, then detailed feature examination is possible, but the process becomes time-consuming and impractical for large numbers of features
Solution Approach 1:
The patent replaces manual mechanical analysis of software features with automated computational processing. By implementing algorithms that automatically extract, standardize, and compare feature vectors, the system maintains detailed feature examination capabilities while eliminating the time-consuming manual review process, enabling efficient comparison of hundreds or thousands of features across multiple software projects.
Solution Approach 2:
The patent creates standardized feature vector copies that represent the essential characteristics of software features in a compact, comparable format. By generating these simplified representations that capture key feature attributes, the system enables rapid automated comparison while preserving the detailed information needed for accurate feature analysis, avoiding the need to manually examine complete feature sets.
Data Source
AI summary
A searchable database of software features for software projects can be automatically built in some examples. One such example can involve analyzing descriptive information about a software project to determine software features of the software project. Then a feature vector for the software project can be generated based on the software features of the software project. The feature vector can be stored in a database having multiple feature vectors for multiple software projects. The multiple feature vectors can be easily and quickly searched in response to search queries.