Code Converter Using Neural Vectors to Resolve Development Conflicts

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Solution Overview

Problem

Software development processes often lead to conflicts and redundancies when multiple developers simultaneously modify the same code, resulting in inconsistent or inappropriate functionality due to uncoordinated changes.

Innovation Solution

A machine learning-based code converter that parses and converts software code into vectors using a neural network to identify and resolve conflicts and redundancies by comparing changes and detecting similarities, thereby preventing inappropriate implementations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

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

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 converting code portions and their changes into vectors and comparing them in advance, the system detects potential conflicts and redundancies before they cause functional inconsistencies, allowing developers to proceed with simultaneous modifications confidently.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback to developers about detected conflicts and redundancies in code changes. By comparing vector representations of code portions and changes, the system identifies similarities that indicate potential issues and communicates these findings to developers, enabling them to adjust their changes to maintain code consistency.

Inventive Principle:

Principle #23Feedback

2Reliability

If manual code review processes are used to detect conflicts, then code quality improves, but development time increases

Engineering Contradiction:

Improvecode qualityVSAvoiddevelopment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces manual code review processes with an automated machine learning-based system. By converting code and changes into vector representations and using computational comparison to detect conflicts and redundancies, the system achieves thorough code quality checking without requiring significant additional development time, as the automated process operates efficiently without human intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If comprehensive code analysis is performed to detect all conflicts, then code consistency improves, but computational complexity increases

Engineering Contradiction:

Improvecode consistencyVSAvoidcomputational complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system transforms code from its original textual form into vector representations, changing the parameter space in which code analysis occurs. This transformation enables efficient comparison and detection of conflicts and redundancies by operating on numerical vectors rather than raw code text, reducing computational complexity while maintaining comprehensive analysis capabilities.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11347500B2Software code converter for resolving conflicts during code development

Publication Date: 2022.05.31 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).