Revisiting prior empirical findings for mobile apps: an empirical case study on the 15 most popular open-source Android apps
Bibliographic record
Abstract
Our increasing reliance on mobile devices has led to the explosive development of millions of mo-bile apps across multiple platforms that are used by millions of people around the world every day. However, most software engineering research is performed on large desktop or server-side software applications (e.g., Eclipse and Apache). Unlike the software applications that we typically study, mo-bile apps are 1) designed to run on devices with limited, but diverse, resources (e.g., limited screen space and touch interfaces with diverse gestures) and 2) distributed through centralized “app stores,” where there is a low barrier to entry and heavy com-petition. Hence, mobile apps may differ from tradi-tionally studied desktop or server side applications, the extent that existing software development “best practices ” may not apply to mobile apps. There-fore, we perform an exploratory study, comparing mobile apps to commonly studied large applica-tions and smaller applications along two dimen-sions: the size of the code base and the time to fix defects. Finally, we discuss the impact of our findings by identifying a set of unique software en-gineering challenges posed by mobile apps. Copyright c © 2013 Mark D. Syer. Permission to copy is hereby granted provided the original copyright notice is repro-duced in copies made. 1
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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 | 0.025 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".