ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel
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
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it