  Gameplay videos contain rich information about how players interact with the
game and how the game responds. Sharing gameplay videos on social media
platforms, such as Reddit, has become a common practice for many players.
Often, players will share gameplay videos that showcase video game bugs. Such
gameplay videos are software artifacts that can be utilized for game testing,
as they provide insight for bug analysis. Although large repositories of
gameplay videos exist, parsing and mining them in an effective and structured
fashion has still remained a big challenge. In this paper, we propose a search
method that accepts any English text query as input to retrieve relevant videos
from large repositories of gameplay videos. Our approach does not rely on any
external information (such as video metadata); it works solely based on the
content of the video. By leveraging the zero-shot transfer capabilities of the
Contrastive Language-Image Pre-Training (CLIP) model, our approach does not
require any data labeling or training. To evaluate our approach, we present the
$\texttt{GamePhysics}$ dataset consisting of 26,954 videos from 1,873 games,
that were collected from the GamePhysics section on the Reddit website. Our
approach shows promising results in our extensive analysis of simple queries,
compound queries, and bug queries, indicating that our approach is useful for
object and event detection in gameplay videos. An example application of our
approach is as a gameplay video search engine to aid in reproducing video game
bugs. Please visit the following link for the code and the data:
https://asgaardlab.github.io/CLIPxGamePhysics/
