Towards a Robust Waiting Strategy for Web GUI Testing for an Industrial Software System
Bibliographic record
Abstract
Automated web GUI testing has been widely adopted since manual testing is time-consuming and tedious. Waiting strategy plays a vital role in automated web GUI testing since it significantly impacts the testing performance. Though important, little focus has been set on the waiting strategies in web GUI testing. Existing waiting strategies either wait for a predetermined time, which is not reliable in a dynamic environment, or only wait for a specific condition to be verified, which is often not robust enough to handle the complicated testing scenarios. In this work, we introduce a robust waiting strategy. Instead of waiting for a predetermined time or waiting for the availability of a particular element, our approach waits for a desired state to reach. This is achieved by capturing the Document Object Models (DOM) at the desired point, followed by an offline analysis to identify the differences between the DOMs associated with every two consecutive test actions. Such differences are used to determine the appropriate waiting time when automatically generating tests. Evaluation results with an industrial web application indicate that our approach produces more robust tests than the conventional waiting strategies used in web GUI testing. Furthermore, our generated tests are more representative of the recorded usage scenarios and are efficient with low overhead in test execution time.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".