Code Coverage Criteria for Asynchronous Programs
Bibliographic record
Abstract
Asynchronous software often exhibits complex and error-prone behaviors that should be tested thoroughly. Code coverage has been the most popular metric to assess test suite quality. However, traditional code coverage criteria do not adequately reflect completion, interactions, and error handling of asynchronous operations. This paper proposes novel test adequacy criteria for measuring: (i) completion of asynchronous operations in terms of both successful and exceptional execution, (ii) registration of reactions for handling both possible outcomes, and (iii) execution of said reactions through tests. We implement JScope, a tool for automatically measuring coverage according to these criteria in JavaScript applications, as an interactive plug-in for Visual Studio Code. An evaluation of JScope on 20 JavaScript applications shows that the proposed criteria can help improve assessment of test adequacy, complementing traditional criteria. According to our investigation of 15 real GitHub issues concerned with asynchrony, the new criteria can help reveal faulty asynchronous behaviors that are untested yet are deemed covered by traditional coverage criteria. We also report on a controlled experiment with 12 participants to investigate the usefulness of JScope in realistic settings, demonstrating its effectiveness in improving programmers’ ability to assess test adequacy and detect untested behavior of asynchronous code.
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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 | 0.005 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".