Understanding User Feedback in Software Ecosystems: A Study on Challenges and Mitigation Strategies
Bibliographic record
Abstract
Abstract Online user feedback has become an essential mechanism for software organizations to gain insight into user concerns and to recognize areas for improvement. In software platform ecosystems, staying abreast of user feedback is particularly challenging due to the multitude of feedback channels and the complex interplay with third party applications. In this paper we report from a mixed-method study of user feedback from over 40,000 relevant reviews from 139 SECO platforms out of 2.4 million online user reviews scraped from 283 retrieved SECO platforms. Through thematic analysis and machine learning classifiers with high accuracy, we identified and analyzed six categories of user challenges in the areas of Integration, Customer Support, Design & Complexity, Privacy & Security, Cost & Pricing, and Performance & Compatibility. Our analysis also shows a significant growth of SECO user feedback in the past five years, highlighting the importance of understanding such user feedback as well as research methodologies to automatically study online user concerns in software ecosystems. To further understand mitigation strategies for challenges reported by end users, we interviewed four executives from large ecosystems and describe strategies in addressing those identified challenges. This research is a first large scale study of user feedback in software ecosystems; the categories of user concerns are hopefully useful in guiding platforms in designing and fostering better software ecosystems. Our methodology for automatically classifying the user feedback that is SECO-related can also serve as guidance for future studies that can further advance our understanding of user feedback and how to integrate it into improved software ecosystems.
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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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".