Techniques for trusted software engineering
Bibliographic record
Abstract
How do we decide if it is safe to run a given piece of software on our machine? Software used to arrive in shrink-wrapped packages from known vendors. But increasingly, software of unknown provenance arrives over the internet as applets or agents. Running such software risks serious harm to the hosting machine. Risks include serious damage to the system and loss of private information. Decisions about hosting such software are preferably made with good knowledge of the software product itself, and of the software process used to build it. We use the term Trusted Software Engineering to describe tools and techniques for constructing safe software artifacts in a manner designed to inspire trust in potential hosts. Existing approaches have considered issues such as schedule, cost and efficiency; we argue that the traditionally software engineering issues of configuration management and intellectual property protection are also of vital concern. Existing approaches (e.g., Java) to this problem have used static type checking, run-time environments, formal proofs and/or cryptographic signatures; we propose the use of trusted hardware in combination with a key management infrastructure as an additional, complementary technique for trusted software engineering, which offers some attractive features.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| 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".