BUGSJS: a benchmark and taxonomy of JavaScript bugs
Bibliographic record
Abstract
Summary JavaScript is a popular programming language that is also error‐prone due to its asynchronous, dynamic, and loosely typed nature. In recent years, numerous techniques have been proposed for analyzing and testing JavaScript applications. However, our survey of the literature in this area revealed that the proposed techniques are often evaluated on different datasets of programs and bugs. The lack of a commonly used benchmark limits the ability to perform fair and unbiased comparisons for assessing the efficacy of new techniques. To fill this gap, we propose BugsJS, a benchmark of 453 real, manually validated JavaScript bugs from 10 popular JavaScript server‐side programs, comprising 444k lines of code (LOC) in total. Each bug is accompanied by its bug report, the test cases that expose it, as well as the patch that fixes it. We extended BugsJS with a rich web interface for visualizing and dissecting the bugs' information, as well as a programmable API to access the faulty and fixed versions of the programs and to execute the corresponding test cases, which facilitates conducting highly reproducible empirical studies and comparisons of JavaScript analysis and testing tools. Moreover, following a rigorous procedure, we performed a classification of the bugs according to their nature. Our internal validation shows that our taxonomy is adequate for characterizing the bugs in BugsJS. We discuss several ways in which the resulting taxonomy and the benchmark can help direct researchers interested in automated testing of JavaScript applications. © 2021 The Authors. Software Testing, Verification & Reliability published by 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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".