Glitch Detection System Using Machine Learning for Video Game Debugging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Debugging complex video game environments with thousands of components is time-consuming and costly, often only identifying some glitches before release or after updates, due to the inefficiency of manual testing processes.
Innovation Solution
A computer-implemented method using a machine learning model to analyze frames rendered by a game engine, identifying and classifying graphical glitches such as corrupted, stretched, low-resolution, missing, or placeholder textures, with a confidence score to reduce false positives and generate automated bug reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing processes are used to debug game environments, then developers can identify some glitches before release, but the process becomes extremely time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical testing processes with an automated machine learning-based inspection system. The machine learning model automatically analyzes rendered frames to detect graphical glitches, substituting human inspectors and manual testing procedures with an automated computational system that processes visual data to identify defects in the virtual environment.
Solution Approach 2:
The inspection system performs self-service by automatically detecting and classifying graphical glitches without requiring continuous human intervention. The machine learning model autonomously analyzes frames, identifies defects, and generates inspection results, enabling the system to serve its own quality assurance function independently.
2Reliability
If manual testing processes are used to debug game environments, then some glitches can be detected, but the process is costly and may only identify limited glitches
Solution Approach 1:
The patent replaces expensive manual testing processes with an automated machine learning inspection system. This substitution reduces costs by eliminating or minimizing human labor while improving detection completeness through the model's ability to systematically analyze all rendered frames for various types of graphical glitches including texture errors, geometry issues, and rendering artifacts.
Solution Approach 2:
The system creates visual copies of the game environment through rendered frames that can be analyzed by the machine learning model. These frame copies serve as test specimens that can be inspected automatically, allowing multiple analyses without additional cost or time for re-running the actual game environment.
3Measurement precision
If a machine learning model analyzes frames to classify graphical glitches, then detection accuracy improves, but false positives may occur
Solution Approach 1:
The inspection system implements feedback mechanisms where the machine learning model's classifications are validated and refined. The system can use confidence scores to determine when additional verification is needed, and feedback from validated results is used to continuously improve the model's accuracy and reduce false positives through iterative training and adjustment.
Data Source
AI summary
The present disclosure provides a system for automating graphical testing during video game development. The system can use Deep Convolutional Neural Networks (DCNNs) to create a model to detect graphical glitches in video games. The system can use an image, a video game frame, as input to be classified into one of defined number of classifications. The classifications can include a normal image and one of a plurality of different kinds of glitches. In some embodiments, the glitches can include corrupted textures, including low resolution textures and stretched textures, missing textures, and placeholder textures. The system can apply a confidence measure to the analysis to help reduce the number of false positives.