From requirements to software trustworthiness using scenarios and finite state machine
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
The notion of software trustworthiness evaluation in the literature is inherently subjective. It depends on how the software is used and in what context it is used. Moreover different users evaluate a software system according to different criteria, point of view and background. Therefore to assess the software trustworthiness, it is not wise to look for a general set of characteristics and parameters; instead, there is need to define a model that is tailored to the functional and quality requirements that the software has to fulfill. This paper shows a way to model software trustworthiness by using Finite State Machine (FSM) notation and scenarios. The approach introduces a novel behavioristic model for verifying software trustworthiness based on scenarios of interactions between the software and its users and environment. These interactions consist of simple scenarios of examples or counterexamples of desired behavior. The approach supports incremental changes in requirements/scenarios. An experiment of application of the model for verifying software trustworthiness based on the scenarios of interactions between the software and its users and environment is presented in a separate case study [40].
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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