Searching Bug Instances in Gameplay Video Repositories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gameplay videos offer valuable insights into player interactions and game responses, particularly data about game bugs. Despite the abundance of gameplay videos online, extracting useful information remains a challenge. This paper introduces a method for searching and extracting relevant videos from extensive video repositories using English text queries. Our approach requires no external information, like video metadata; it solely depends on video content. 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GamePhysics</i> dataset, comprising 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 and compound queries, indicating that our method is useful for detecting objects and events in gameplay videos. Moreover, we assess the effectiveness of our method by analyzing a carefully annotated dataset of 220 gameplay videos. The results of our study demonstrate the potential of our approach for applications such as the creation of a video search tool tailored to identifying video game bugs, which could greatly benefit Quality Assurance (QA) teams in finding and reproducing bugs. The code and data used in this paper can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://zenodo.org/records/10211390</uri>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it