Vejovis: suggesting fixes for JavaScript faults
Bibliographic record
Abstract
JavaScript is used in web applications for achieving rich user interfaces and implementing core functionality. Unfortunately, JavaScript code is known to be prone to faults. In an earlier study, we found that over 65% of such faults are caused by the interaction of JavaScript code with the DOM at runtime (DOM-related faults). In this paper, we first perform an analysis of 190 bug reports to understand fixes commonly applied by programmers to these DOM-related faults; we observe that parameter replacements and DOM element validations are common fix categories. Based on these findings, we propose an automated technique and tool, called Vejovis, for suggesting repairs for DOM-based JavaScript faults. To evaluate Vejovis, we conduct a case study in which we subject Vejovis to 22 real-world bugs across 11 applications. We find that Vejovis accurately suggests repairs for 20 out of the 22 bugs, and in 13 of the 20 cases, the correct fix was the top ranked one.
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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 | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".