A DQCNN-feedback mechanism-based mobile app testing using MFWKLST-based pattern analysis
Bibliographic record
Abstract
User Interface (UI) testing has become a common practice for quality assurance in industrial mobile applications, and many automated tools are used for testing. However, existing research methods do not focus on effective feedback mechanisms to present testing outcomes. Therefore, this research proposes a DQCNN-based feedback mechanism for mobile application testing. Initially, computer screens are pre-processed using SAR-SRGAN and contour formation. Then, patterns are analysed using MFWKLST, followed by GUI element recognition using the YOLO approach. From the identified GUI elements, backgrounds are subtracted and elements are classified as text, image, and click action using the DB-CD-SCAN approach. Features are then extracted from the text and images, and important features are selected using CST. Meanwhile, coordinates are detected from click actions using CD, and robotic movement is assessed based on these coordinates. Finally, the selected features and robotic movements are provided to DQCNN to generate feedback, which is returned to the robotic movement. The proposed method achieved 99.07% accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".