A Model for Reasoning about JavaScript Promises
Why this work is in the frame
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Bibliographic record
Abstract
In JavaScript programs, asynchrony arises in situations such as web-based user-interfaces, communicating with servers through HTTP requests, and non-blocking I/O. Event-based programming is the most popular approach for managing asynchrony, but suffers from problems such as lost events and event races, and results in code that is hard to understand and debug. Recently, ECMAScript 6 has added support for promises, an alternative mechanism for managing asynchrony that enables programmers to chain asynchronous computations while supporting proper error handling. However, JavaScript promises are complex and error-prone in their own right, so programmers would benefit from techniques that can reason about the correctness of promise-based code. Since the ECMAScript 6 specification is informal and intended for implementors of JavaScript engines, it does not provide a suitable basis for formal reasoning. This paper presents lambda_P, a core calculus that captures the essence of EcmaScript 6 promises. Based on lambda_P, we introduce the promise graph, a program representation that can assist programmers with the debugging of promise-based code. We then report on a case study in which we investigate how the promise graph can be helpful for debugging errors in promise-based code fragments reported on the StackOverflow forum. As another application of lambda_P, we systematically derive a static analysis that can be used to compute an over-approximation of the promise graph.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 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