Atrina: Inferring Unit Oracles from GUI Test Cases
Bibliographic record
Abstract
Testing JavaScript web applications is challenging due to its complex runtime interaction with the Document Object Model (DOM). Writing unit-level assertions for JavaScript applications is even more tedious as the tester needs to precisely understand the interaction between the DOM and the JavaScript code, which is responsible for updating the DOM. In this work, we propose to leverage existing DOM-dependent assertions in a human-written UI-based test cases as well as useful execution information inferred from the UI-based test suite to automatically generate assertions used for unit-level testing of the JavaScript code of the application. Our approach is implemented in a tool called ATRINA. We evaluate our approach to assess its effectiveness. The results indicate that ATRINA maps DOM-based assertions to the corresponding JavaScript code with high accuracy (99% precision, 92% recall). In terms of fault finding capability, the assertions generated by ATRINA outperform human-written DOM-based assertions by 31% on average. It also surpasses the state-of-the-art mutation-based assertion generation technique by 26% on average in detecting faults.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".