Web Canvas Testing Through Visual Inference
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
Canvas elements are one of the major web technologies for creating high-performance graphics and visualizations in the browser. The canvas provides APIs for directly painting on the screen, but does not have a DOM state. As such, common web testing techniques that rely on the DOM cannot be applied to canvas elements. Furthermore, there has been little to no research in the literature for testing canvas elements. We propose an automated approach for testing canvas elements and their properties. Our approach performs a visual analysis of the screenshots of canvas elements and infers visual objects, their attributes, and their hierarchical relationships present on the canvas. Each inferred object is then represented as an augmented element inside the canvas element on the DOM tree. Finally, tests are generated from the augmented canvas DOM with assertions that check the inferred objects. We implement this approach in a tool, CanvaSure, and evaluate its accuracy and effectiveness for testing canvas-based applications. Our evaluation results show that CanvaSure has an accuracy of 91% for visually inferring the contents of the canvas, and is capable of correctly detecting 93% of injected visual faults on canvas applications.
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