STRE: An Automated Approach to Suggesting App Developers When to Stop Reading Reviews
Bibliographic record
Abstract
It is well known that user feedback (i.e., reviews) plays an essential role in mobile app maintenance. Users upload their troubles, app issues, or praises, to help developers refine their apps. However, reading tremendous amounts of reviews to retrieve useful information is a challenging job. According to our manual studies, reviews are full of repetitive opinions, thus developers could stop reading reviews when no more new helpful information appears. Developers can extract useful information from partial reviews to ameliorate their app and then develop a new version. However, it is tough to have a good trade-off between getting enough useful feedback and saving more time. In this paper, we propose a novel approach, named STRE, which utilizes historical reviews to suggest the time when most of the useful information appears in reviews of a certain version. We evaluate STRE on 62 recent versions of five apps from Apple's App Store. Study results demonstrate that our approach can help developers save their time by up to 98.33% and reserve enough useful reviews before stopping to read reviews such that developers do not spend additional time in reading redundant reviews over the suggested stopping time. At the same time, STRE can complement existing review categorization approaches that categorize reviews to further assist developers. In addition, we find that the missed top-word-related reviews appearing after the suggested stopping time contain limited useful information for developers. Finally, we find that 12 out of 13 of the emerging bugs from the studied versions appear before the suggested stopping time. Our approach demonstrates the value of automatically refining information from reviews.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".