An exploratory study of MVC-based architectural patterns in Android apps
Bibliographic record
Abstract
Mobile app development now represents a significant part of the software industry, with Android being the largest ecosystem. Android development comes with its own design practices and templates (layouts, activities, etc.). Developers also use different established architectural patterns for designing interactive software such as MVC, MVP and MVVM. They implement these patterns based on their understanding and experience. Thus, the choice and the implementation of such patterns varies from a developer to another. To the best of our knowledge, there is no work that provides a comprehensive view of the use of these patterns in mobile apps. Moreover, there is no clear understanding of which pattern to use and what is the trend for designing mobile apps using such patterns. In this paper, we propose an automatic approach to identify which MVC-based architectural pattern (MVC, MVP and MVVM) is used predominantly in a given app. For this purpose, we defined each of these patterns through a number of heuristics according to the pattern's potential implementations within the Android framework. We conducted an empirical study on a large set of mobile apps downloaded from the Google Play Store. We found, not surprisingly, a dominance of the popular MVC pattern, a rare use of MVP while MVVM is almost unused and a significant number of apps do not follow any pattern. The empirical study also enabled us to analyse the use of these patterns by domain, size and last-update date of the apps. We observed that MVC has been the most used pattern over the past years and it continues to gain popularity, and that small-size apps are mostly the ones that do not use any pattern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".