AutoFLox: An Automatic Fault Localizer for Client-Side JavaScript
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
Java Script is a scripting language that plays a prominent role in modern web applications today. It is dynamic, loosely typed, and asynchronous. In addition, it is extensively used to interact with the DOM at runtime. All these characteristics make Java Script code error-prone and challenging to debug. Java Script fault localization is currently a tedious and mainly manual task. Despite these challenges, the problem has received very limited attention from the research community. We propose an automated technique to localize Java Script faults based on dynamic analysis of the web application, tracing, and backward slicing of Java Script code. Our fault localization approach is implemented in an open source tool called Auto Lox. The results of our empirical evaluation indicate that (1) DOM-related errors are prominent in web applications, i.e., they form at least 79% of reported Java Script bugs, (2) our approach is capable of automatically localizing DOM-related Java Script errors with a high degree of accuracy (over 90%) and no false-positives, and (3) our approach is capable of isolating Java Script errors in a production web application, viz., Tumbler.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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