Automatic fault localization for client‐side JavaScript
Bibliographic record
Abstract
Summary JAVASCRIPTis a scripting language that plays a prominent role in web applications today. It is dynamic, loosely typed and asynchronous and is extensively used to interact with the Document Object Model (DOM) at runtime. All these characteristics makeJAVASCRIPTcode error‐prone; unfortunately,JAVASCRIPTfault localization remains a tedious and mainly manual task. Despite these challenges, the problem has received very limited research attention. This paper proposes an automated technique to localizeJAVASCRIPTfaults based on dynamic analysis, tracing and backward slicing ofJAVASCRIPTcode. This technique is capable of handling features ofJAVASCRIPTcode that have traditionally been difficult to analyse, includingeval, anonymous functions and minified code. The approach is implemented in an open source tool calledAUTOFLOX, and evaluation results indicate that it is capable of (1) automatically localizing DOM‐relatedJAVASCRIPTfaults with high accuracy (over 96%) and no false‐positives and (2) isolatingJAVASCRIPTfaults in production websites and actual bugs from real‐world web applications. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".