On the Relationship between User Churn and Software Issues
Bibliographic record
Abstract
The satisfaction of users is only part of the success of a software product, since a strong competition can easily detract users from a software product/service. User churn is the jargon used to denote when a user changes from a product/service to the one offered by the competition. In this study, we empirically investigate the relationship between the issues that are present in a software product and user churn. For this purpose, we investigate a new dataset provided by the alternativeto.net platform. Alternativeto.net has a unique feature that allows users to recommend alternatives for a specific software product, which signals the intention to switch from one software product to another. Through our empirical study, we observe that (i) the intention to change software is tightly associated to the issues that are present in these software; (ii) we can predict the rate of potential churn using machine learning models; (iii) the longer the issue takes to be fixed, the higher the chances of user churn; and (iv) issues within more general software modules are more likely to be associated with user churn. Our study can provide more insights on the prioritization of issues that need to be fixed to proactively minimize the chances of user churn.
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 | 0.006 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".