Emulation of Transient Software Faults for Dependability Assessment: A Case Study
Why this work is in the frame
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Bibliographic record
Abstract
Fault Tolerance Mechanisms (FTMs) are extensively used in software systems to counteract software faults, in particular against faults that manifest transiently, namely Mandelbugs. In this scenario, Software Fault Injection (SFI) plays a key role for the verification and the improvement of FTMs. However, no previous work investigated whether SFI techniques are able to emulate Mandelbugs adequately. This is an important concern for assessing critical systems, since Mandelbugs are a major cause of failures, and FTMs are specifically tailored for this class of software faults. In this paper, we analyze an existing state-of-the-art SFI technique, namely G-SWFIT, in the context of a real-world fault-tolerant system for Air Traffic Control (ATC). The analysis highlights limitations of G-SWFIT regarding its ability to emulate the transient nature of Mandelbugs, because most of injected faults are activated in the early phase of execution, and they deterministically affect process replicas in the system. We also notice that G-SWFIT leaves untested the 35% of states of the considered system. Moreover, by means of an experiment, we show how emulation of Mandelbugs is useful to improve SFI. In particular, we emulate concurrency faults, which are a critical sub-class of Mandelbugs, in a fully representative way. We show that proper fault triggering can increase the confidence in FTMs' testing, since it is possible to reduce the amount of untested states down to 5%.
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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 | 0.001 | 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