Bibliographic record
Abstract
While providing better performance, transparency and expressiveness, the main features of the web technologies such as web caching, session and cookies, dynamically generated web pages etc. may also affect the correct understanding of the web applications running on top of them. From the viewpoint of formal verification and specification-based testing, this suggests that the formal model of the web application we use for static analysis or test case generation should contain the abstract behavior of the underlying web application environment. Here we consider the automated generation of such a model in terms of extended finite state machines from a given abstract description of a web application by incorporating the abstract behavioral model of the web browsers in the presence of session/cookies and dynamically generated web pages. The derived model can serve as the formal basis for both model checking and specification-based testing on the web applications where we take into account the effect of the internal caching mechanism to the correct accessibility of the web pages, which can be quite sensitive to the security of the information they carry. In order to check the correctness of the derived model against required properties, we provide the automated translation of the model into Promela. By applying SPIN on Promela models, we present experimental results on the evaluation of the proposed modeling in terms of scalability.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Z543. Source: Masters Abstracts International, Volume: 43-05, page: 1761. Adviser: Jessica Chen. Thesis (M.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".