Feedback-directed exploration of web applications to derive test models
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
Dynamic exploration techniques play a significant role in automated web application testing and analysis. However, a general web application crawler that exhaustively explores the states can become mired in limited specific regions of the web application, yielding poor functionality coverage. In this paper, we propose a feedback-directed web application exploration technique to derive test models. While exploring, our approach dynamically measures and applies a combination of code coverage impact, navigational diversity, and structural diversity, to decide a-priori (1) which state should be expanded, and (2) which event should be exercised next to maximize the overall coverage, while minimizing the size of the test model. Our approach is implemented in a tool called FEEDEx. We have empirically evaluated the efficacy of FEEDEx using six web applications. The results show that our technique is successful in yielding higher coverage while reducing the size of the test model, compared to classical exhaustive techniques such as depth-first, breadth-first, and random exploration.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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