A study of the relation of mobile device attributes with the user-perceived quality of Android apps (journal-first abstract)
Bibliographic record
Abstract
The number of mobile apps and the number of mobile devices have increased considerably in the past few years. To succeed in the competitive market of mobile apps, such as Google Play Store, developers should improve the user-perceived quality of their apps. In this paper, we investigate the relationship between mobile device attributes and the user-perceived quality of Android apps. We observe that the user-perceived quality of apps varies across devices. Device attributes, such as the CPU and the screen resolution, share a significant relationship with the user-perceived quality. However, having a better characteristic of an attribute, such as a higher display resolution, does not necessarily share a positive relationship with the user-perceived quality. App developers should not only consider the app attributes but also consider the device attributes of the available devices to deliver high-quality apps. The original paper is published in the Empirical Software Engineering journal communicated by Lin Tan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".