XR Code Error Resolution via ML Discrepancy Highlighting
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Solution Overview
Problem
Manual error resolution in extended reality environments is inefficient, leading to delays and human errors due to the need for manual recreation of code discrepancies during application validation processes.
Innovation Solution
An automated system utilizing machine learning algorithms and an extended reality platform to identify and resolve code errors by building displays based on application requirements and source code, highlighting discrepancies, and suggesting alterations for implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual error resolution is used in extended reality environments, then human operators can identify and correct code discrepancies, but the process is inefficient and leads to delays and human errors
Solution Approach 1:
The system enables automated self-correction of code errors through machine learning algorithms that autonomously identify discrepancies between application requirements and source code, and automatically generate resolution actions without requiring manual human intervention for each error
Solution Approach 2:
The patent replaces manual mechanical processes of code review and error correction with automated computational systems using machine learning models that analyze code patterns, identify errors, and propose corrections algorithmically
2Loss of time
If automated machine learning algorithms are used to identify code errors, then error resolution time is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary automated analysis of code errors using pre-trained machine learning models, identifying and flagging discrepancies before manual review, thus reducing the time required for error resolution while managing complexity through automated preprocessing
Solution Approach 2:
The patent introduces an intermediate layer of machine learning algorithms that act as mediators between the raw code and human reviewers, automatically filtering and prioritizing errors to reduce the burden on human operators while maintaining system manageability
3Measurement precision
If real-time visualization of code discrepancies is provided in extended reality environment, then error identification accuracy is improved, but computational resources required increase
Solution Approach 1:
The system segments the code analysis process into distinct modules that operate independently in the extended reality environment, allowing parallel processing of different code sections and reducing overall computational resource requirements while maintaining high error identification accuracy
Solution Approach 2:
The patent utilizes the extended reality environment to visualize code errors in three-dimensional spatial representations, adding a dimensional aspect to error identification that improves accuracy while distributing computational load across the XR system's graphical processing capabilities
Data Source
AI summary
Systems, computer program products, and methods are described herein for automated code resolution in an extended reality environment. The present invention allows a user (such as a software developer) to view source code discrepancies in real time using an extended reality (XR) environment. In this regard, the present invention focuses on electronic applications (and the electronic work products/electronic data hosted thereon) and represents a combined view of real-time applications and application requirements within an XR environment. A user may then visualize discrepancies between the current application and the application requirements via an XR platform (accessible using a virtual/augmented/mixed reality device) and proactively make edits, approvals, or otherwise interact with said application. The system may also be configured to automatically alter the source code to resolve said discrepancies.