Mining Kbuild to Detect Variability Anomalies in Linux
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
The Linux kernel is extensively specialized or configured so that it can be used for many purposes. This variability is implemented by means of three distinct artifacts: source code files, Kconfig (configuration) files, and Make files. Any inconsistencies between these three can lead to undesirable anomalies which can lead to increased maintenance efforts or decreased reliability. This paper extends published work that had found anomalies (dead and undead code blocks) by concentrating largely on code and Kconfig files. We detect further anomalies in the Linux kernel when we also consider the Make files. At the level of code blocks, our work exposes many additional anomalies -- more than we could study manually. We found that when we lift the level from code blocks to code files, the detected anomalies became easier to study and understand and thus more useful to the developer. By means of examples, we illustrate how the anomalies we detect can lead to undesired behavior. We show how, over time, developers tend to find and delete such anomalies. We suggest that automatic detection of such anomalies has the potential to decrease maintenance efforts and increase reliability.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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