Graph Database Source Code Analysis for Large Repository Queries
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Solution Overview
Problem
Existing source code analysis solutions fail to provide comprehensive insights and are resource-intensive, particularly when analyzing large code repositories, and custom query generation is complicated.
Innovation Solution
A computer system that analyzes source code by extracting node, edge, and bindings data, storing them in a graph database structure, and generating metrics data, allowing for efficient and intuitive query execution using a graph query language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing source code analysis solutions are used, then basic code analysis is possible, but comprehensive insights are not provided and custom query generation is complicated
Solution Approach 1:
The patent changes the fundamental data structure parameter from relational database to graph database, which fundamentally alters how code analysis queries are formulated and executed. This parameter change enables comprehensive insights while simplifying query generation by using intuitive graph traversal syntax instead of complex relational SQL queries.
Solution Approach 2:
The patent introduces an intermediary layer - the graph database structure - that mediates between the source code and the analysis queries. This intermediary transforms complex code relationships into intuitive graph structures, making comprehensive analysis accessible through simple queries without requiring users to understand complex underlying codebases.
2Productivity
If existing source code analysis solutions are used, then analysis can be performed, but execution is costly in terms of computer time and resources
Solution Approach 1:
The patent performs preliminary action by pre-processing source code into a graph database structure during the analysis setup phase. This pre-transformation of code into graph format enables fast query execution during actual analysis, as queries can directly traverse the pre-built graph structure without re-parsing and processing the entire codebase for each query.
Solution Approach 2:
The patent changes the data storage parameter from traditional relational database to graph database, which fundamentally improves query execution performance. The graph structure enables direct traversal of code relationships without the need for complex joins and subqueries, significantly reducing computational resources and execution time.
3Quantity of substance
If entire code repository is analyzed, then comprehensive coverage is achieved, but analysis difficulty increases significantly
Solution Approach 1:
The patent segments the code repository into a graph database structure where individual code elements (nodes) and their relationships (edges) are independently represented. This segmentation allows comprehensive coverage of the entire repository while maintaining simple query execution, as users can query specific segments of the graph without needing to understand the complexity of the entire codebase.
Solution Approach 2:
The patent introduces the graph database as an intermediary that simplifies the analysis of large code repositories. The graph structure transforms complex code relationships into intuitive visual and queryable representations, making comprehensive repository analysis accessible and manageable without increasing perceived complexity for users.
Data Source
AI summary
A computer system for analyzing source code is disclosed. The computer system includes a processor and electronic memory storage. The electronic memory storage includes source code and executable instructions. The processor runs the executable instructions to: access the source code from the electronic memory storage; analyze code elements of the accessed source code to extract node data, edge data, and bindings data; and store the node data, edge data, and bindings data, in a graph database structure in the electronic memory storage.