Graph-Based Vectorization for Software Code Optimization
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Solution Overview
Problem
Optimizing software code to reduce resource consumption and execution time is not a precise science, often resulting in inefficient code that consumes more resources and takes longer to execute than necessary.
Innovation Solution
A software code optimizer that converts code into a graph, then into vectors and matrices, allowing for automatic detection and correction of inefficiencies by comparing with optimized matrices stored in a database, and applying changes to improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual code optimization is performed, then code efficiency can be improved, but the process is imprecise and often done incorrectly or suboptimally
Solution Approach 1:
The patent replaces manual code optimization (mechanical human intervention) with an automated system that converts code to graphs, generates matrices, and performs database lookups. This substitution eliminates the imprecision of manual optimization by using algorithmic processes instead of human judgment.
Solution Approach 2:
The system enables code to optimize itself automatically through a deterministic process: converting code to graphical representation, generating matrices from graphs, querying databases for optimized versions, and applying corrections. The code optimization process serves itself without external human intervention, achieving precision through automation.
2Loss of time
If code is executed without optimization, then execution time and resource consumption increase, but optimization processes are complex and error-prone
Solution Approach 1:
The patent segments the code optimization process into distinct, manageable components: code-to-graph conversion, graph-to-matrix generation, database querying, and code correction. This segmentation reduces the perceived complexity by breaking down the optimization system into modular, independent steps that can be executed sequentially.
Solution Approach 2:
The patent introduces intermediate representations (graphs and matrices) as mediators between the original code and the optimization database. These intermediaries simplify the comparison and lookup processes by transforming code into a standardized format that can be efficiently matched against database entries, reducing the overall system complexity.
3Use of energy by moving object
If manual optimization methods are used, then resource consumption increases, but automated methods require complex conversion processes
Solution Approach 1:
The patent performs preliminary actions by pre-converting code into graph and matrix representations before the actual optimization lookup. By preparing these intermediate forms in advance, the system enables efficient database queries and comparisons, reducing memory consumption during the actual optimization process while managing conversion complexity through structured preprocessing.
Data Source
AI summary
A software code optimizer automatically detects inefficiencies in software code and corrects them. Generally, the software code optimizer converts software code into a graph representing the workflows and relationships in the software code. The graph is then converted into vectors that represent each workflow in the software code. The vectors are assembled into a matrix that represents the software code. The matrix may be stored in a cluster in a database as an example of optimized software code or be compared with other matrices stored as clusters in the database to determine whether the software code is optimized. The software code optimizer can change the software code to be more efficient if a matrix for an optimized version of the software code is found in the database.