Towards a taxonomy of Roxygen documentation in R packages
Bibliographic record
Abstract
Abstract Software documentation is often neglected, impacting maintenance and reuse and leading to technical issues. In particular, when working with scientific software, such issues in the documentation pose a risk to producing reliable scientific results as they may cause improper or incorrect use of the software. R is a popular programming language for scientific software with a prolific package-based ecosystem, where users contribute packages (i.e., libraries). R packages are intended to be reused, and their users rely extensively on the available documentation. Thus, understanding what information developers provide in their packages’ documentation (generally, through a system known as Roxygen, based on Javadoc) is essential to contribute to it. This study mined 379 GitHub repositories of R packages and analysed a sample to develop a taxonomy of natural language descriptions used in Roxygen documentation. This was done through hybrid card sorting, which included two experienced R developers. The resulting taxonomy covers parameters, returns, and descriptions, providing a baseline for further studies. Our taxonomy is the first of its kind for R. Based on previous studies in pure object-oriented languages, our taxonomy could be extensible to other dynamically-typed languages used in scientific programming.
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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 | 0.022 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| 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".