Mutation Analysis for Assessing End-to-End Web Tests
Bibliographic record
Abstract
End-to-end UI testing plays a significant role in the regression testing of web apps, in order to validate end user functionality. Because of their importance, UI test suites are often created and maintained manually by employing browser automation tools such as selenium. However, currently, there exists no reliable method to ascertain the fault-finding capabilities for UI test suite of any given web app. Mutation testing, a well known fault-based testing technique for assessment of test suite adequacy, relies on generating mutants by making small changes to source code imitating programmer errors. However, mutation testing is difficult to employ for any given web app because of the heterogeneous nature of the multiple server-side and client-side components they can contain. In this work, we present MaewU, a mutation analysis framework for assessing web UI test suites, which is applicable to any web app as it mutates the dynamic DOM in the browser instead of the source code. We propose 16 mutation operators that mutate the behaviour and appearance of web elements to mimic the nine categories of web app faults found through an analysis of 250 bug reports. We evaluate our dynamic mutation analysis framework on six open source web apps. The results from our empirical evaluation demonstrate that MaewU is effective in assessing Web UI test suites in terms of adequacy and facilitates test suite quality improvements.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".