Detecting Inconsistencies in JavaScript MVC Applications
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
Higher demands for more reliable and maintainable JavaScript-based web applications have led to the recent development of MVC (Model-View-Controller) frameworks. One of the main advantages of using these frameworks is that they abstract out DOM API method calls, which are one of the leading causes of web application faults, due to their often complicated interaction patterns. However, MVC frameworks are susceptible to inconsistencies between the identifiers and types of variables and functions used throughout the application. In response to this problem, we introduce a formal consistency model for web applications made using MVC frameworks. We propose an approach -- called Aurebesh -- that automatically detects inconsistencies in such applications. We evaluate Aurebesh by conducting a fault injection experiment and by running it on real applications. Our results show that Aurebesh is accurate, with an overall recall of 96.1% and a precision of 100%. It is also useful in detecting bugs, allowing us to find 15 real-world bugs in applications built on Angular JS, a popular MVC framework.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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