Emulation of Transient Software Faults for Dependability Assessment: A Case Study
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".