Bibliographic record
Abstract
This is the replication package for our paper on dependency smells. Here is a short description of what is contained in this package: Code This folder contains the code used for extracting, parsing, and analyzing the smells in the dataset, along with statistical analyses. The "parser.py" parses the project information (such as package.json) and prepares them in the databases. The "analyzer.py" file is responsible for the majority of the empirical analyses. Datasets This folder contains the intermediate datasets created and used in our analyses. The "smelldataset.db" file contains all smelly and clean dependencies for the latest snapshot. The "smell_counts.csv" file contains smells statistics for the projects in our dataset. The "changehistory.db" file contains the historical smell statistics for a period of 5 years. The code also requires the GhTorrent Dataset available at: https://ghtorrent.org/downloads.html. Survey Questionnaires and Responses These two folders contain the full set of questions that we asked the developers in our surveys along with the responses. The responses have been anonymised to hide personal information. Tool This is the published tool which is also available at: https://github.com/abbasjavan/DependencySniffer Visualization Scripts This folder contains the scripts used to create the figures for the paper.
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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.062 |
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".