An approach to extract RESTful services from web applications
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
The web is the largest database with a huge amount of information and services primarily intended for human users. A user performs different tasks on the web, such as reserving a table in a restaurant. The reuse of web application components would offer greater productivity and ease the maintenance of web applications. The focus of this paper is to circumvent this limitation by proposing an approach to interactively identify reusable web tasks in a web application. We perform a case study on 21 real world web applications from four domains. We identify tasks and services from these web applications. Results show that our proposed approach can identify tasks correctly with a precision of 89% and a recall of more than 90%. Our proposed approach identifies relations among tasks with a precision of 86% and 100% recall. Our approach semi-automatically extracts reusable tasks and represent each task as a RESTful service.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it