A model for reasoning about JavaScript promises
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, 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 implementers of JavaScript engines, it does not provide a suitable basis for formal reasoning. This paper presents λ p , a core calculus that captures the essence of ECMAScript 6 promises. Based on λ p , we introduce the promise graph, a program representation that can assist programmers with 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 related to promises in code fragments posted to the StackOverflow website.
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.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".