WAVI: A reverse engineering tool for 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
Web developers face some unique challenges when trying to understand, modify and document the structure of their web applications. The heterogeneity and complexity of the underlying technologies and languages heighten comprehension problems. In particular, JavaScript, as an essential part of the Web ecosystem, is a language that offers a flexibility that can make its code hard to grasp, when it comes to comprehension and documentation tasks. In this paper, we present the first iteration of WAVI (WebAppViewer), a reverse engineering tool that uses static analysis and a filter-based mechanism to retrieve and document the structure of a Web application. WAVI is able to extract elements coming from essential web languages and frameworks such as HTML, JavaScript, CSS and Node.js. The tool makes use of some simple, effective heuristics to accurately retrieve dependency links for files and methods. WAVI also offers the visualisation of the extracted information as force-directed graphs and customized class diagrams. The effectiveness of WAVI is evaluated with experiments that demonstrate that (i) it can resolve JavaScript calls better than a recent technique, and (ii) its visualisation modules are intuitive and scalable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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