DWASTIC: Automating Coverage Metrics for Dynamic 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
Abstract—Building comprehensive test suites for web applications poses new challenges in software testing. Coverage criteria used for traditional systems to assess the quality of test cases are simply not sufficient for complex dynamic applications. As a result, faults in web applications can often be traced to insufficient testing coverage of the complex interactions between the components. This paper presents a new set of coverage criteria for web applications, based on page access, use of server variables, and interactions with the database. Following an instrumentation transformation to insert dynamic tracking of these aspects, a static analysis is used to automatically create a coverage database by extracting and executing only the instrumentation statements of the program. The database is then updated dynamically during execution by the instrumentation calls themselves. We have evaluated the usefulness of our coverage criteria and the feasibility and precision of our approach on the popular bulletin board web application PhpBB.
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 | 0.001 |
| 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