Software Runs Everything Off-World: Let’s Make Sure It’s Correct and Secure
Bibliographic record
Abstract
It is no exaggeration to say that software drives everything off-world, and no space mission can succeed without it. As earth-bound societies accelerate space exploration, it is critical that mission software be correct and secure. Because of the high stakes, considerable time and energy is expended in space software verification and security analysis. But faults and compromises in operational software persist despite best efforts. These risks are being magnified by the growth in autonomous and AI-generated software. Current verification methods will always be useful, but given the high consequences of failure, a need exists for more comprehensive and efficient verification techniques. The emerging technology of software behavior computation holds promise to fill this gap. The mathematics-based behavior computation process, known as Function Extraction (FX), produces the as-built specification of a program, whether human- or AI-generated. FX provides domain-to-range coverage of all behavior and subsumes all test cases that could be executed. Computed behavior is a new software engineering artifact that can enable a new approach to functional verification and security analysis. The computations reveal how variable values are computed in programs, not just their final values as in testing. This paper describes behavior computation technology as implemented in an FX prototype, and illustrates the process for 1) controlling complexity for human understanding, 2) scaling computations across program hierarchies, 3) verifying behavior of an imagined space habitat environmental controller, and 4) computing the behavior of AI-generated code prior to use.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".