Automatic migration from synchronous to asynchronous JavaScript APIs
Bibliographic record
Abstract
The JavaScript ecosystem provides equivalent synchronous and asynchronous Application Programming Interfaces (APIs) for many commonly used I/O operations. Synchronous APIs involve straightforward sequential control flow that makes them easy to use and understand, but their "blocking" behavior may result in poor responsiveness or performance. Asynchronous APIs impose a higher syntactic burden that relies on callbacks, promises, and higher-order functions. On the other hand, their nonblocking behavior enables applications to scale better and remain responsive while I/O requests are being processed. While it is generally understood that asynchronous APIs have better performance characteristics, many applications still rely on synchronous APIs. In this paper, we present a refactoring technique for assisting programmers with the migration from synchronous to asynchronous APIs. The technique relies on static analysis to determine where calls to synchronous API functions can be replaced with their asynchronous counterparts, relying on JavaScript's async/await feature to minimize disruption to the source code. Since the static analysis is potentially unsound, the proposed refactorings are presented as suggestions that must be reviewed and confirmed by the programmer. The technique was implemented in a tool named Desynchronizer. In an empirical evaluation on 12 subject applications containing 316 synchronous API calls, Desynchronizer identified 256 of these as candidates for refactoring. Of these candidates, 244 were transformed successfully, and only 12 resulted in behavioral changes. Further inspection of these cases revealed that the majority of these issues can be attributed to unsoundness in the call graph.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".