Examining User-Developer Feedback Loops in the iOS App Store
Bibliographic record
Abstract
Application Stores, such as the iTunes App Store, give developers access to their users’ complaints and requests in the form of app reviews. However, little is known about how developers are responding to app reviews. Without such knowledge developers, users, App Stores, and researchers could build upon wrong foundations. To address this knowledge gap, in this study we focus on feedback loops, which occur when developers address a user concern. To conduct this study we use both supervised and unsupervised methods to automatically analyze a corpus of 1752 different apps from the iTunes App Store consisting of 30,875 release notes and 806,209 app reviews. We found that 18.7% of the apps in our corpus contain instances of feedback loops. In these feedback loops we observed interesting behaviors. For example, (i) feedback loops with feature requests and login issues were twice as likely as general bugs to be fixed by developers, (ii) users who reviewed with an even tone were most likely to have their concerns addressed, and (iii) the star rating of the app reviews did not influence the developers likelihood of completing a feedback loop.
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 | 0.008 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".