A dataset of feature additions and feature removals from the Linux kernel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes a dataset of feature additions and removals in the Linux kernel evolution history, spanning over seven years of kernel development. Features, in this context, denote configurable system options that users select when creating customized kernel images. The provided dataset is the largest corpus we are aware of capturing feature additions and removals, allowing researchers to assess the kernel evolution from a feature-oriented point-of-view. Furthermore, the dataset can be used to better understand how features evolve over time, and how different artifacts change as a result. One particular use of the dataset is to provide a real-world case to assess existing support for feature traceability and evolution. In this paper, we detail the dataset extraction process, the underlying database schema, and example queries. The dataset is directly available at our Bitbucket repository: https://bitbucket.org/lpassos/kconfigdb
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it