Reliability Models Applied to Mobile Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart phones have become the most used electronic devices. They carried out most of the functionalities of desktops, allowing various useful applications that suit the users' needs. Therefore, instead of the operator, the user has become the number one controller of the device and its applications and thus its reliability becomes an emergent need. We aim to investigate and evaluate the efficacy of Software Reliability Growth Models (SRGMs) when applied to Smart phone application failure data and check whether they achieve the same success as in the desktop/laptop area. We selected three of the most used SRGMs and applied them to three different Smart phone applications. None of the selected models were able to account for the data satisfactorily. Their failure is traced back to the specific features of mobile applications compared to desktop applications. Thus, a suitable model for Smart phone applications is still needed to improve their reliability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it