AutoFLox: An Automatic Fault Localizer for Client-Side JavaScript
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".