On the expressiveness of business process modeling notations for software requirements elicitation
Bibliographic record
Abstract
Business process models have proved to be useful for requirements elicitation. Since software development depends on the quality of the requirements specifications, generating high-quality business process models is therefore critical. A key factor for achieving this is the expressiveness in terms of completeness and clarity of the modeling notation for the domain being modeled. The Bunge-Wand-Weber (BWW) representation model is frequently used for assessing the expressiveness of business process modeling notations. This article presents some propositions to adapt the BWW representation model to allow its application to the software requirements elicitation domain. These propositions are based on the analysis of the Guide to the Software Engineering Body of Knowledge (SWEBOK) and the Guide to the Business Analysis Body of Knowledge (BABOK). The propositions are validated next by experts in business process modeling and software requirements elicitation. The results show that the BWW representation model requires to be specialized by including concepts specific to software requirements elicitation.
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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 | 0.054 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".