Code Converter Vectorizing Software for Conflict Detection

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Solution Overview

Problem

Software code development often leads to conflicts and redundancies when multiple developers simultaneously modify the same code, resulting in inconsistent or inappropriate functionality, as changes may be made without coordination, leading to conflicts or redundant features.

Innovation Solution

A machine learning-based code converter that vectorizes software code using a neural network to identify and resolve conflicts and redundancies by comparing numerical representations of code changes, preventing conflicts and redundancies through automatic detection and alerting developers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If multiple developers simultaneously modify the same code, then development productivity increases, but code conflicts and redundancies occur leading to functional inconsistencies

Engineering Contradiction:

Improvedevelopment speedVSAvoidcode consistency

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary analysis of code changes before they are committed to the repository. By vectorizing and comparing code representations in advance, the system detects potential conflicts and redundancies, allowing developers to resolve issues before they affect code consistency and functionality.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides automatic feedback to developers about detected conflicts and redundancies in their code changes. This feedback mechanism enables developers to adjust their changes to maintain code consistency while continuing to work collaboratively and productively on the same codebase.

Inventive Principle:

Principle #23Feedback

2Reliability

If manual code review processes are used to detect conflicts, then code consistency is maintained, but development time increases

Engineering Contradiction:

Improvecode consistencyVSAvoiddevelopment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces manual code review processes with an automated machine learning-based analysis system. The neural network vectorizes code and automatically detects conflicts and redundancies, eliminating the time-consuming manual review process while maintaining or improving code consistency detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transforms code into vector representations and uses mathematical distance measurements to detect similarities and conflicts. This parameter transformation enables automated, rapid comparison of code changes without requiring manual inspection, significantly reducing development time while maintaining reliability.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If developers implement changes without coordination, then ease of operation is improved, but conflicts and redundancies arise causing functional errors

Engineering Contradiction:

Improvecode modification easeVSAvoidcode conflicts

Core Design Contradiction:

Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The system applies preliminary anti-action by detecting and flagging potential conflicts and redundancies before they can cause functional errors. By identifying problematic code changes in advance through vector comparison, the system prevents harmful effects while allowing developers to continue making independent, uncoordinated changes with confidence.

Inventive Principle:

Principle #9Preliminary anti-action

Data Source

PatentUS11573775B2Software code converter for resolving redundancy during code development

Publication Date: 2023.02.07 BANK OF AMERICA CORP

AI summary

A code converter uses machine learning to determine conflicts and redundancies in software code. Generally, the code converter uses machine learning to convert software code into vectors that represent the code. These vectors may then be compared with other vectors to determine similarities between code. The similarities may be used to detect conflicts and/or redundancies created during the development process (e.g., when a developer attempts to change the code).