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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".