An Empirical Study of Client-Side JavaScript Bugs
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
Context: Client-side JavaScript is widely used in web applications to improve user-interactivity and minimize client-server communications. Unfortunately, web applications are prone to JavaScript faults. While prior studies have demonstrated the prevalence of these faults, no attempts have been made to determine their root causes and consequences. Objective: The goal of our study is to understand the root causes and impact of JavaScript faults and how the results can impact JavaScript programmers, testers and tool developers. Method: We perform an empirical study of 317 bug reports from 12 bug repositories. The bug reports are thoroughly examined to classify and extract information about the fault's cause (the error) and consequence (the failure and impact). Result: The majority (65%) of JavaScript faults are DOM-related, meaning they are caused by faulty interactions of the JavaScript code with the Document Object Model (DOM). Further, 80% of the highest impact JavaScript faults are DOM-related. Finally, most JavaScript faults originate from programmer mistakes committed in the JavaScript code itself, as opposed to other web application components such as the server-side or HTML code. Conclusion: Given the prevalence of DOM-related faults, JavaScript programmers need development tools that can help them reason about the DOM. Also, testers should prioritize detection of DOM-related faults as most high impact faults belong to this category. Finally, developers can use the error patterns we found to design more powerful static analysis tools for JavaScript.
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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 | 0.000 | 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 | 0.000 |
| 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