Finding broken promises in asynchronous JavaScript programs
Bibliographic record
Abstract
Recently, promises were added to ECMAScript 6, the JavaScript standard, in order to provide better support for the asynchrony that arises in user interfaces, network communication, and non-blocking I/O. Using promises, programmers can avoid common pitfalls of event-driven programming such as event races and the deeply nested counterintuitive control ow referred to as “callback hell”. Unfortunately, promises have complex semantics and the intricate control– and data- ow present in promise-based code hinders program comprehension and can easily lead to bugs. The promise graph was proposed as a graphical aid for understanding and debugging promise-based code. However, it did not cover all promise-related features in ECMAScript 6, and did not present or evaluate any technique for constructing the promise graphs. In this paper, we extend the notion of promise graphs to include all promise-related features in ECMAScript 6, including default reactions, exceptions, and the synchronization operations race and all. Furthermore, we report on the construction and evaluation of PromiseKeeper, which performs a dynamic analysis to create promise graphs and infer common promise anti-patterns. We evaluate PromiseKeeper by applying it to 12 open source promise-based Node.js applications. Our results suggest that the promise graphs constructed by PromiseKeeper can provide developers with valuable information about occurrences of common anti-patterns in their promise-based code, and that promise graphs can be constructed with acceptable run-time overhead.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".