On the evolution of Linux kernels: a complex network perspective
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Bibliographic record
Abstract
SUMMARY This paper presents a novel method to study the evolution of Linux kernel components using complex networks to understand how Linux kernel components evolve over time. After analyzing the node degree distribution, clustering coefficient, and average path length of the call graphs corresponding to the kernel components of 130 development versions and 94 stable versions (V1.0 to V2.4.35), we found that the call graphs of the file system, driver, kernel, memory management, and net components are scale‐free, small‐world complex networks. In addition, all of the five components exhibit very strong preferential attachment tendency. With such in‐depth understanding of the features of the Linux kernel components, we propose a generic method that could be used to find major structural changes that occur during the evolution of software systems. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 |
| 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