Bibliographic record
Abstract
SUMMARY Although build systems control what code gets compiled into the final built product, they are often overlooked when studying software variability. The Linux kernel is one of the biggest open source software systems supporting variability and contains over 10,000 configurable features described in its Kconfig files. To understand the role of the build system in variability implementation, we use Linux as a case study. We study its build system, Kbuild , and extract the variability constraints in its Makefiles. We first provide a quantitative analysis of the variability in Kbuild . We then study how the variability constraints in the build system affect variability anomalies detected in Linux. We concentrate on dead and undead artifacts, and by extending previous work, we show that considering build system variability constraints allows more anomalies to be detected. We provide examples of such anomalies on both the code block and source file level. Our work shows that Kbuild contains a large percentage of the variability information in Linux, so it should not be ignored during variability analysis. Nonetheless, the anomalies we find suggest that variability on the file level in Kbuild is consistent with Kconfig , whereas the constraints on the code level are harder to keep consistent with both Kbuild and Kconfig . Copyright © 2013 John Wiley & Sons, Ltd.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".