Bibliographic record
Abstract
The Linux kernel has long been an interesting subject of study in terms of its source code. Recently, it has also been studied in terms of its variability since the Linux kernel can be configured to include or omit certain features according to the user's selection. These features are defined in the Kconfig files included in the Linux kernel code. Several articles study both the source code and Kconfig files to ensure variability is correctly implemented and to detect anomalies. However, these studies ignore the Make files which are another important component that controls the variability of the Linux kernel. The Make files are responsible for specifying what actually gets compiled and built into the final kernel. With over 1,300 Make files, more than 35,000 source code files, and over 10,000 Kconfig features, inconsistencies and anomalies are inevitable. In this paper, we explore the Linux's Make files (Kbuild) to detect anomalies. We develop three rules to identify anomalies in the Make files. Using these rules, we detect 89 anomalies in the latest release of the Linux kernel (2.6.38.6). We also perform a longitudinal analysis to study the evolution of Kbuild anomalies over time, and the solutions implemented to correct them. Our results show that many of the anomalies we detect are eventually corrected in future releases. This work is a first attempt at exploring the consistency of the variability implemented in Kbuild with the rest of the kernel. Such work opens the door for automatic anomaly detection in build systems which can save developers time in the future.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".