Multi-layer software configuration: Empirical study on wordpress
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
Software can be adapted to different situations and platforms by changing its configuration. However, incorrect configurations can lead to configuration errors that are hard to resolve or understand, especially in the case of multi-layer architectures, where configuration options in each layer might contradict each other or be hard to trace to each other. Hence, this paper performs an empirical study on the occurrence of multi-layer configuration options across Wordpress (WP) plugins, WP, and the PHP engine. Our analyses show that WP and its plugins use on average 76 configuration options, a number that increases across time. We also find that each plugin uses on average 1.49% to 9.49% of all WP database options, and 1.38% to 15.18% of all WP configurable constants. 85.16% of all WP database options, 78.88% of all WP configurable constants, and 52 PHP configuration options are used by at least two plugins at the same time. Finally, we show how the latter options have a larger potential for questions and confusion amongst users.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it