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
Testing JavaScript code is important. JavaScript has grown to be among the most popular programming languages and it is extensively used to create web applications both on the client and server. We present the first empirical study of JavaScript tests to characterize their prevalence, quality metrics (e.g. code coverage), and shortcomings. We perform our study across a representative corpus of 373 JavaScript projects, with over 5.4 million lines of JavaScript code. Our results show that 22% of the studied subjects do not have test code. About 40% of projects with JavaScript at client-side do not have a test, while this is only about 3% for the purely server-side JavaScript projects. Also tests for server-side code have high quality (in terms of code coverage, test code ratio, test commit ratio, and average number of assertions per test), while tests for client-side code have moderate to low quality. In general, tests written in Mocha, Tape, Tap, and Nodeunit frameworks have high quality and those written without using any framework have low quality. We scrutinize the (un)covered parts of the code under test to find out root causes for the uncovered code. Our results show that JavaScript tests lack proper coverage for event-dependent callbacks (36%), asynchronous callbacks (53%), and DOM-related code (63%). We believe that it is worthwhile for the developer and research community to focus on testing techniques and tools to achieve better coverage for difficult to cover JavaScript code.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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