Codebase Dependency Graphs for Faster Refactoring Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identifying and analyzing code dependencies in large-scale software projects is a time-consuming, resource-intensive, and error-prone process, leading to inefficiencies and security risks due to redundant code files and manual dependency management.
Innovation Solution
An automated system generates dependency graphs and matrices to visualize and analyze codebase dependencies, reducing manual effort and resource consumption by identifying and removing unused code files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual dependency analysis is performed in large-scale codebases, then developers can identify dependencies for migration and refactoring, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational analysis using Abstract Syntax Trees (AST) and dependency graphs. The system automatically parses code files, builds AST representations, and generates dependency graphs to identify relationships between code components, eliminating the need for manual time-consuming analysis while maintaining high accuracy in dependency identification.
Solution Approach 2:
The patent creates structural copies of code elements through AST representations and dependency graph models. By copying the logical structure of code files into these abstract representations, the system enables efficient automated analysis of dependencies without directly manipulating the original code, significantly reducing analysis time while preserving accuracy.
2Reliability
If comprehensive dependency analysis is performed across the entire codebase, then all dependencies are identified, but computing resources are heavily consumed
Solution Approach 1:
The patent divides the codebase into individual code files and processes them separately to build ASTs and identify dependencies. By segmenting the analysis into file-level units rather than analyzing the entire codebase monolithically, the system reduces peak computing resource consumption while maintaining comprehensive dependency identification through the aggregation of individual file analyses into a complete dependency graph.
3Object-affected harmful factors
If redundant code files are identified and removed, then security risks are minimized and performance is optimized, but the risk of removing needed files increases
Solution Approach 1:
The patent implements feedback mechanisms where the dependency graph analysis provides information about which code files are actually used by other parts of the system. This feedback allows the system to distinguish between truly redundant files and files that are still needed, enabling safe removal of unused files while preserving codebase integrity. The automated analysis feedback ensures that migration and refactoring decisions are based on accurate dependency information.
Data Source
AI summary
Embodiments are directed to an automated identification and analysis of dependencies in a codebase. In particular, dependency graphs are automatically generated to represent the dependencies in a format that may be used to generate dependency-based output. Dependency-based output may include any type of output that indicates or represents data or analysis associated with dependencies in a codebase. In some cases, a dependency graph may be used to generate a representation or indication of dependency data or analysis. For example, a dependency matrix may be generated using a dependency graph and provided as output to represent various dependency data.