Mutation-Based Testing of Format String Bugs
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
Format string bugs (FSBs) make an implementation vulnerable to numerous types of malicious attacks. Testing an implementation against FSBs can avoid consequences due to exploits of FSBs such as denial of services, corruption of application states, etc. Obtaining an adequate test data set is essential for testing of FSBs. An adequate test data set contains effective test cases that can reveal FSBs. Unfortunately, traditional techniques do not address the issue of adequate testing of an application for FSB. Moreover, the application of source code mutation has not been applied for testing FSB. In this work, we apply the idea of mutation-based testing technique to generate an adequate test data set for testing FSBs. Our work addresses FSBs related to ANSI C libraries. We propose eight mutation operators to force the generation of adequate test dataset. A prototype mutation-based testing tool named MUFORMAT is developed to generate mutants automatically and perform mutation analysis. The proposed operators are validated by using four open source programs having FSBs. The results indicate that the proposed operators are effective for testing FSBs.
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 | 0.001 |
| 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