From requirements to software trustworthiness using scenarios and finite state machine
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].
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".