Multi-layer software configuration: Empirical study on wordpress
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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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 | 0.009 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".