Invalid Graphical Representation Bug Detection Model

This model is trained to detect Invalid Graphical Representation issues in video games. It identifies bugs where aspects of the world state are incorrectly rendered. For example, it can detect when a character is performing a swimming animation while on land, or when a clothing item is not appearing as intended in the game.

The model is based on concepts from the paper "What went wrong; the taxonomy of video game bugs".

Model Details

• Model Type: Classification (Binary)
• Training Data: Trained on a dataset of video game bugs, specifically focused on Invalid Graphical Representation as described in the paper "What went wrong; the taxonomy of video game bugs".
• Task: Bug detection in video games.
• Intended Use: This model is designed for game developers and QA teams to automate the detection of rendering issues or animation bugs in video games.

Training Metrics

The model was trained for 4 epochs, with the following performance metrics:

Key Metrics:

• F1 Score: Balances precision and recall, with a final value of 0.8791 after 4 epochs.
• Precision: The accuracy of positive predictions.
• Recall: The ability to detect true positives.

Intended Audience

• Game Developers: To detect graphical and animation bugs in video games.
• Quality Assurance (QA) Teams: To automate the detection of rendering or animation issues during game testing.
• Researchers: Interested in analyzing or extending bug detection models for video games.

Limitations:

• This model is specifically trained to detect Invalid Graphical Representation bugs, focusing on issues like animation errors and missing rendered items.
• It may not generalize well to other types of video game bugs outside of this category.
• Performance can vary depending on the game context and rendering engine. Further fine-tuning may be required for use in different games.

How to Use

You can use the model for binary classification to predict whether a given game state exhibits an Invalid Graphical Representation bug. Here's an example using the Hugging Face transformers library:

from transformers import pipeline

# Load the model from Hugging Face
bug_detection = pipeline('text-classification', model='fyp-buglens/VideoGameReviews-InvalidGraphicalRepresentation-TinyBERT')

# Example usage
result = bug_detection("A character is swimming on land")
print(result)  # Output: label indicating if it's a bug or not


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license: apache-2.0
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