MASS: A tool for Mutation Analysis of Space CPS
Bibliographic record
Abstract
We present MASS, a mutation analysis tool for embedded software in cyber-physical systems (CPS). We target space CPS (e.g., satellites) and other CPS with similar characteristics (e.g., UAV).Mutation analysis measures the quality of test suites in terms of the percentage of detected artificial faults. There are many mutation analysis tools available but they are inapplicable to CPS because of scalability and accuracy challenges.To overcome such limitations, MASS implements a set of optimization techniques that enable the applicability of mutation analysis and address scalability and accuracy in the CPS context. MASS has been successfully evaluated on a large study involving embedded software systems provided by industry partners; the study includes an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library provided by the European Space Agency. A demo video of MASS is available at https://www.youtube.com/watch?v=gC1x9cU0-tU.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".