Modelling assumptions and requirements in the context of project risk
Bibliographic record
Abstract
Many researchers have emphasized the importance of documenting assumptions (As) underlying software requirements (Rs). However, As and Rs can change with time for reasons such as: (i) an A or R was elicited incorrectly and subsequently needs to be changed; (ii) operational domain changes induce changes in the A and R sets; and (iii) the change in validity of an A, or desirability of an R, respectively, causes the validity of another A or desirability of an R to change. In Section 2, we describe our model and how it works. To put such a model into practice, we need to consider at least two scenarios. One is intra-release cycle-time, where invalidity risk is predicted at the start of the project for times between the inception and completion of the project. This would give us intra-release risk trends. The second scenario is prediction over multiple releases. This would give us a risk trend over a longer period of time. The full paper describes an algorithm to cover both of these scenarios and gives an example (from a banking application) of how the model could apply in practice. Here, we consider only the first scenario due to limitation of space.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".