Software Component Search Query Parsing
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Solution Overview
Problem
Current software component search systems are inefficient and error-prone due to the need for multiple focused queries, resulting in significant delays and potential quality and security issues in software development, as developers face the daunting task of reviewing millions of search results to find the right components.
Innovation Solution
A system that parses software component search queries using machine learning techniques to split queries into multiple entities, enhance keywords, and execute parallel searches across technology, natural language, and programming language entities, providing a more accurate and efficient search result by prioritizing relevant components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a developer creates multiple focused queries to find unique software components, then the search precision is improved, but the time required to review results and the complexity of the search process increases significantly
Solution Approach 1:
The system segments a complex search query into multiple entity types (technology entities, natural language entities, programming language entities) and processes each segment through specialized parsers. This segmentation allows the system to maintain high search precision by targeting specific entity types while avoiding the need for developers to manually create multiple separate queries, thus reducing the time required to review results.
Solution Approach 2:
The patent introduces an intermediary parsing system that acts as a mediator between the developer's intent and the search engine. This intermediary automatically divides the query into focused searches across different entity types and synthesizes results, eliminating the need for developers to manually manage multiple queries and their corresponding result sets.
2Ease of operation
If a developer creates a longer query with all requirements to reduce the number of queries, then the number of queries is reduced, but the number of search results increases to millions making it hard to find the right match
Solution Approach 1:
The system segments the search process by creating distinct parsing pathways for different entity types (technology, natural language, programming language). Each segment is processed independently with appropriate weighting, allowing the system to handle complex queries with multiple requirements while controlling the volume of results by focusing on relevant entity types rather than returning all possible matches.
Solution Approach 2:
The patent applies local quality by assigning different weights and processing methods to different entity types within the query. Technology entities, natural language entities, and programming language entities are handled with specialized parsers and weighting schemes, ensuring that each part of the query contributes appropriately to the final results without overwhelming the system with irrelevant matches.
3Productivity
If regular expression based parsing is used to process search queries, then the parsing process is simple and fast, but the keyword usage is not enhanced and search accuracy is limited
Solution Approach 1:
The system segments the parsing process into three distinct pathways: technology keyword parsing, natural language parsing, and programming language parsing. Each pathway uses appropriate techniques for its specific entity type, maintaining parsing efficiency while significantly improving search accuracy through enhanced keyword processing and entity-specific analysis.
Solution Approach 2:
The patent changes the parameters of the parsing process by applying different parsing strategies and weighting schemes to different entity types. Instead of a uniform regular expression approach, the system dynamically adjusts parsing parameters based on the entity type, enhancing keyword usage and search accuracy while maintaining productivity through automated processing.
4Adaptability or versatility
If web-crawling software is used to index textual webpage content from multiple internet sources, then the coverage of search sources is improved, but the complexity of processing and organizing the data increases
Solution Approach 1:
The system segments the data processing complexity by creating separate parsing and processing pathways for different entity types extracted from web sources. Technology entities, natural language entities, and programming language entities are processed independently through specialized parsers, reducing the overall complexity while maintaining comprehensive coverage of multiple internet sources.
Data Source
AI summary
Systems and methods for parsing a software component search query to enable multi entity searches are provided. An exemplary method includes filtering a search query for unwanted characters, correcting a spelling of the search query, applying a first machine learning model to remove filter terms from the search query, parsing the query to extract entities, categorizing the entities as natural language, programming language, or keyword text, applying a second machine learning model to enhance search characteristics of the entities characterized as natural language, applying a third machine learning model to enhance search characteristics of the entities characterized as programming language, applying a fourth machine learning model to enhance search characteristics of the entities categorized as keyword text, consolidating the enhanced entities into an enhanced search query, applying a fifth machine learning model to determine intended sources for the search query, and searching the intended sources for results.