Efficient construction of approximate call graphs for JavaScript IDE services
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
The rapid rise of JavaScript as one of the most popular programming languages of the present day has led to a demand for sophisticated IDE support similar to what is available for Java or C#. However, advanced tooling is hampered by the dynamic nature of the language, which makes any form of static analysis very difficult. We single out efficient call graph construction as a key problem to be solved in order to improve development tools for JavaScript. To address this problem, we present a scalable field-based flow analysis for constructing call graphs. Our evaluation on large real-world programs shows that the analysis, while in principle unsound, produces highly accurate call graphs in practice. Previous analyses do not scale to these programs, but our analysis handles them in a matter of seconds, thus proving its suitability for use in an interactive setting.
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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