Bibliographic record
Abstract
Today's web applications are deployed on powerful software stacks such as MEAN (JavaScript) or LAMP (PHP), which consist of multiple layers such as an operating system, web server, database, execution engine and application framework, each of which provide resources to the layer just above it. These powerful software stacks unfortunately are plagued by so-called cross-stack configuration errors (CsCEs), where a higher layer in the stack suddenly starts to behave incorrectly or even crash due to incorrect configuration choices in lower layers. Due to differences in programming languages and lack of explicit links between configuration options of different layers, sysadmins and developers have a hard time identifying the cause of a CsCE, which is why this paper (1) performs a qualitative analysis of 1,082 configuration errors to understand the impact, effort and complexity of dealing with CsCEs, then (2) proposes a modular approach that plugs existing source code analysis (slicing) techniques, in order to recommend the culprit configuration option. Empirical evaluation of this approach on 36 real CsCEs of the top 3 LAMP stack layers shows that our approach reports the misconfigured option with an average rank of 2.18 for 32 of the CsCEs, and takes only few minutes, making it practically useful.
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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 | 0.008 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".