Graph-based vectorization for software code optimizations

Abstract

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.

Description

TECHNICAL FIELD[0001]This disclosure relates generally to optimizing computer software code.BACKGROUND[0002]Software developers develop software applications by writing software code.SUMMARY OF THE DISCLOSURE[0003]Software developers develop software applications by writing software code. The software code is written to perform certain desired functionality when executed. There may be several different ways to write software code that performs the same function or achieves the same result. In other words, two software developers can write two different pieces of software code that perform the same function or achieve the same result. However, one piece of software code may be more efficient (e.g., less memory consumption, less time of execution, etc.) than the other. In these situations, the less efficient piece of software code can be optimized by changing the software code to execute more efficiently. Optimizing software code, however, is not a precise science and often is done in...

AI Technical Summary

Benefits of technology

This patent text discusses the issue of software code optimization, which is important for reducing resource consumption and execution time. The text suggests that certain embodiments improve the efficiency of computer software by making changes to the code to reduce memory usage and execution time. These technical improvements can lead to faster and more resource-efficient software applications.

Problems solved by technology

Optimizing software code, however, is not a precise science and often is done incorrectly or suboptimally.

As a result, when the software code is executed, it consumes more resources than it needs and / or takes more time than it needs.

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