What About Our Bug? A Study on the Responsiveness of NPM Package Maintainers
Bibliographic record
Abstract
Background: Widespread use of third-party libraries makes ecosystems like Node Package Manager (npm) critical to modern software development. However, this interconnected chain of dependencies also creates challenges: bugs in one library can propagate downstream, potentially impacting many other libraries that rely on it. We hypothesize that maintainers may not always decide to fix a bug, especially if the maintainer decides it falls out of their responsibility within the chain of dependencies. Aims: To confirm this hypothesis, we investigate the responsiveness of 30,340 bug reports across 500 of the most depended-upon npm packages. Method: We adopt a mixedmethod approach to mine repository issue data and perform qualitative open coding to analyze reasons behind unaddressed bug reports. Results: Our findings show that maintainers are generally responsive, with a median project-level responsiveness of 70% (IQR: 55-89%), reflecting their commitment to support downstream developers. Conclusions: We present a taxonomy of the reasons some bugs remain unresolved. The taxonomy included contribution practices, dependency constraints, and library-specific standards as reasons for not being responsive. Understanding maintainer behavior can inform practices that promote a more robust and responsive open-source ecosystem that benefit the entire community.
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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 | 0.020 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".