ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel
Bibliographic record
Abstract
Highly configurable systems are highly complex systems. The Linux kernel is arguably one of the most well-known examples. Given its vast configuration space, researchers have used it to conduct many empirical studies as well as to build dedicated methods and tools for analyzing, configuring, testing, optimizing, and maintaining the kernel. However, despite a large body of work, mainly bug fixes that were the result of such research made it back into the kernel's source tree. Unfortunately, Linux users still struggle with kernel configuration and resolving configuration conflicts, since the kernel largely lacks automated support. Additionally, there are technical and community requirements for supporting automated conflict resolution in the kernel, for example, using a pure C-based solution that uses only compatible third-party libraries (if any). With the aim of contributing back to the Linux community, we present ConfigFix, a tooling that we integrated with the Linux kernel configurator, that is purely implemented in C, and that is finally a working solution able to produce fixes for configuration conflicts. We describe our experiences of building upon the large body of research done on the kernel configuration mechanisms as well as how we designed and realized ConfigFix while adhering to the Linux kernel's community requirements and standards. ConfigFix not only helps Linux kernel users obtain their desired configuration, but our implemented semantic abstraction provides the basis for many of the above techniques supporting kernel configuration.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".