RESTful API-based software interface testing techniques and common problems analysis
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
Based on the common problems of the original fuzzy testing technique and the needs of RESTful API fuzzy testing, this paper proposes a white-box fuzzy testing method of REST API based on graph resource nodes for RESTful API software interface testing by using EvoMaster as a basic tool.The effectiveness of the fuzzy testing technique in this paper is analyzed.21 apps with millions of downloads obtain more than 65,000 web request data and more than 8.5GB HAR files, and an average of 2,966 web request data is collected for each app.The REST interface filtering method of this paper's fuzzy testing approach effectively and accurately targets interface objects for fuzzy testing.The number of generated requests of the REST API white-box fuzzing test method based on graph resource nodes in this paper is much lower than that of other tools, and the efficiency of vulnerability discovery is much higher than that of other tools.The test method in this paper improves the number of lines of code covered in six hours by an average of 53.86% over other tools.The test method in this paper can identify more vulnerabilities and can cover all the vulnerabilities found.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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