Toward Using Package Centrality Trend to Identify Packages in Decline
Bibliographic record
Abstract
Due to their increasing complexity, today’s software systems are frequently built by leveraging reusable code in the form of libraries and packages. Software ecosystems (e.g.,npm) are the primary enablers of this code reuse, providing developers with a platform to share their own and use others’ code. These ecosystems evolve rapidly: developers add new packages every day to solve new problems or provide alternative solutions, causing obsolete packages to decline in their importance to the community. Developers should avoid depending on packages in decline, as these packages are reused less over time and may become less frequently maintained. However, current popularity metrics (e.g., Stars, and Downloads) are not fit to provide this information to developers because their semantics do not aptly capture shifts in the community interest. In this article, we propose a scalable approach that uses the package’s centrality in the ecosystem to identify packages in decline. We evaluate our approach with thenpmecosystem and show that the trends of centrality over time can correctly distinguish packages in decline with an ROC–AUC of 0.9. The approach can capture 87% of the packages in decline, on average 18 months before the trend is shown in currently used package popularity metrics. We implement this approach in a tool that can be used to augment thenpmsmetrics and help developers avoid packages in decline when reusing packages fromnpm.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".