Software Assurance in an Uncertain World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
From financial services platforms to social networks to vehicle control, software has come to mediate many activities of daily life. Governing bodies and standards organizations have responded to this trend by creating regulations and standards to address issues such as safety, security and privacy. In this environment, the compliance of software development to standards and regulations has emerged as a key requirement. Compliance claims and arguments are often captured in assurance cases, with linked evidence of compliance. Evidence can come from testcases, verification proofs, human judgment, or a combination of these. That is, experts try to build (safety-critical) systems carefully according to well justified methods and articulate these justifications in an assurance case that is ultimately judged by a human. Yet software is deeply rooted in uncertainty; most complex open-world functionality (e.g., perception of the state of the world by a self-driving vehicle), is either not completely specifiable or it is not cost-effective to do so; software systems are often to be placed into uncertain environments, and there can be uncertainties that need to be We argue that the role of assurance cases is to be the grand unifier for software development, focusing on capturing and managing uncertainty. We discuss three approaches for arguing about safety and security of software under uncertainty, in the absence of fully sound and complete methods: assurance argument rigor, semantic evidence composition and applicability to new kinds of systems, specifically those relying on ML.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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