Crawling Ajax-Based Web Applications through Dynamic Analysis of User Interface State Changes
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
Using JavaScript and dynamic DOM manipulation on the client side of Web applications is becoming a widespread approach for achieving rich interactivity and responsiveness in modern Web applications. At the same time, such techniques---collectively known as Ajax ---shatter the concept of webpages with unique URLs, on which traditional Web crawlers are based. This article describes a novel technique for crawling Ajax -based applications through automatic dynamic analysis of user-interface-state changes in Web browsers. Our algorithm scans the DOM tree, spots candidate elements that are capable of changing the state, fires events on those candidate elements, and incrementally infers a state machine that models the various navigational paths and states within an Ajax application. This inferred model can be used in program comprehension and in analysis and testing of dynamic Web states, for instance, or for generating a static version of the application. In this article, we discuss our sequential and concurrent Ajax crawling algorithms. We present our open source tool called Crawljax , which implements the concepts and algorithms discussed in this article. Additionally, we report a number of empirical studies in which we apply our approach to a number of open-source and industrial Web applications and elaborate on the obtained results.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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