Dependency Update Strategies and Package Characteristics
Bibliographic record
Abstract
This is the replication package for our paper on predicting dependency update strategies. Here is a short description of what is contained in this package: <strong>Pre-processing Code</strong> The data_preparation.py file filters the initial libraries.io dataset to only include relevant columns for the npm packages. The feature_extraction.py file includes the extraction of model features. The feature_process.py file includes the majority of preprocessing scripts to derive new features, clean-up missing values and prepare the data for the models. <strong>ML Models</strong> The models.py file contains the scripts for training, validating and evaluating the random forest model and the two baselines (stratified random and SemVer only models) used for the study. <strong>Datasets</strong> The raw dataset can be downloaded from libraries.io. The Processed_Project_Features[SP51][RT].csv dataset is the result of all preprocessing steps and is used in the model feed function. <strong>Visualization Scripts</strong> The visualizer.py file includes the visualization scripts used for the paper. <strong>Sampled Packages</strong> The complete set of visualizations for the 160 sampled packages in RQ3.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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; both teacher heads agree on what is shown here.
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".