Merge‐Tree: Visualizing the integration of commits into Linux
Bibliographic record
Abstract
Abstract With an average of more than 900 merges into the Linux kernel per release, many containing hundreds of commits and some containing thousands, maintenance of older versions of the kernel becomes nearly impossible. Various commercial products, such as the Android platform, run older versions of the kernel; due to security, performance, and changing hardware needs, maintainers must understand what changes (commits) are added to the current version of the kernel since the last time they inspected it to make the necessary patches. Current tools provide information about repositories through the directed acyclic graph (DAG) of the repository, which is helpful for smaller projects. However, with the scale and number of branches in the kernel, the DAG becomes overwhelming very quickly. Furthermore, the DAG contains every parents of every commit, while maintainers are more interested in how and when a commit arrives to the official Linux repository. This paper makes 3 contributions: a conversion from DAG to Merge‐Tree, an implementation of a tool built on the Merge‐Tree model, and a user study to evaluate and validate the implementation and model.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".