A case study of using grounded analysis as a requirement engineering method: Identifying personas that specify privacy and security tool users
Bibliographic record
Abstract
This paper explains the importance (1) of full user-space identification with categorization in requirements engineering (RE) and of ensuring that the categorization is a partition of the user space, (2) of the creation and application of user-space-covering personas in RE, (3) of the use of grounded analysis to do RE to produce a specification as a grounded theory, and (4) of privacy and security features in computer-based systems. Then it gives the steps of a grounded analysis method for doing user-space identification with categorization and producing personas as a grounded theory that is describing the classes of users for a computer-based system. The paper summarizes a case study of an iterative application of this method to arrive at a set of user-space-covering personas for privacy and security features in computer-based systems, and it shows how these personas can be used to inform RE for these features. The full case study and the descriptions of the personas are found in the appendices.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".