Source Code Search via Functionality Representation Index
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Solution Overview
Problem
Current technologies fail to effectively organize, index, and make source code searchable using natural language specifications and code graph representation, leading to inefficiencies in aggregating, synthesizing, and analyzing code for easy accessibility and reusability.
Innovation Solution
A computer-implemented method and system that crawls and parses source code units into abstract syntax trees, models them into code graphs, establishes type ontologies, generates semantic IDs through linguistic, structural, and contextual analyses, and organizes data in a functionality representation index for efficient searching and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-driven search is used for source code, then search simplicity is maintained, but search accuracy and relevance deteriorate
Solution Approach 1:
The patent introduces an intermediary layer (functionality representation index and code graph) between the user's natural language query and the source code repository. This intermediary translates semantic queries into structured code representations, enabling accurate search results without requiring complex query parsing or keyword matching algorithms.
Solution Approach 2:
The system performs preliminary actions by pre-processing source code into abstract syntax trees, generating functionality representations, and building the code graph structure before search is needed. This pre-computation enables fast and accurate searching at query time without requiring complex real-time analysis.
2Ease of operation
If natural language query processing is implemented, then ease of use improves, but processing complexity increases
Solution Approach 1:
The patent uses the functionality representation index as an intermediary that bridges natural language queries and code structures. The system translates natural language semantics into code graph queries through this intermediary, allowing users to search using simple language while the complex translation process is handled automatically in the background.
Solution Approach 2:
The system performs self-service by automatically analyzing the semantic meaning of natural language queries and translating them into appropriate code graph search operations without requiring manual intervention or complex configuration. The query processing system adapts to different natural language formulations automatically.
3Measurement precision
If comprehensive code analysis is performed, then search relevance improves, but processing time increases
Solution Approach 1:
The patent performs comprehensive code analysis in advance by parsing source code into abstract syntax trees, extracting functionality representations, and building the code graph structure before search operations. This pre-computation stores detailed code semantics in an optimized format, enabling fast and relevant search results without requiring time-consuming real-time analysis.
Solution Approach 2:
The system dynamically adapts the level of analysis performed based on query requirements. The code graph structure allows the system to traverse only the relevant portions of the codebase needed to answer each query, rather than performing exhaustive analysis on the entire codebase for every search operation.
4Adaptability or versatility
If code is organized by functionality, then reusability improves, but organization complexity increases
Solution Approach 1:
The patent segments source code into functional units by parsing into abstract syntax trees and extracting functionality representations. Each code component is analyzed and organized according to its semantic meaning and purpose, creating a structured breakdown that improves reusability while managing complexity through systematic segmentation.
Solution Approach 2:
The functionality representation index serves as a universal organizing structure that can represent multiple types of code elements (functions, classes, methods, etc.) using a common semantic framework. This universal organization enables different code components to be discovered and reused based on their functionality regardless of their specific type or location in the codebase.
Data Source
AI summary
Systems and methods are disclosed for generating functionality representation, indexing, searching, componentizing, an analyzing source code source code unit in a one or more of code repositories. The systems and method include one or more of crawling a set of data entities in a repository system; parsing said set of data entities into abstract syntax trees (ASTs) architecture; modeling said set of data entities into a code graph (CG) architecture; establishing type ontology (TO) architecture of said set of data entities; organizing and storing said set of data entities in functionality representation index (FRI) architecture; componentizing one or more projects in the repositories into code components; and making the components discoverable by functionality and analyzable for performance, usage volume, etc.