Extraction and comprehension of moodle's access control model: A case study
Bibliographic record
Abstract
Whether for development, maintenance or refactoring, multiple steps in software development cycle require comprehension of a program's access control model (AC model). In this paper, we present a novel approach to reverse-engineer AC model structure from PHP source code. Using an hybrid approach combining static analysis and model checking techniques, we are able to extract AC model structure in a fast and precise way. An experimental tool was developed to evaluate the presented approach and report AC models using source code coloring. For this case study, Moodle, a medium-scale (approx. 625K lines of code), open-source PHP application with a rich AC model was investigated. Results revealed that, although very complex by design, implemented AC models may comparatively be very simple, suggesting that developers tend to maintain a low complexity level when implementing ACs. Detailed figures and distributions are reported. We believe the presented tool and approach may help in understanding and evaluating the implemented AC models in Web systems. Discussion of findings, limitations, and further research are presented.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".