From Early Requirements Modeled by the i* Technique to Later Requirements Modeled in Precise UML
Bibliographic record
Abstract
Requirements capture has been acknowledged as a critical phase of software development, precisely because it is the phase which deals not only with technical knowledge, but also with organizational, managerial, economic and social issues. The emerging consensus is that a requirement specification should include not only software specifications but also business models and other kinds of information describing the context in which the intended system will function. Unfortunately, the current dominant object oriented modeling technique, i.e. Unified Modeling Technique, is ill equipped for capturing early requirements which are typically informal and often focus on organisational objectives. UML is more suitable for later phases of requirements capture, which usually focus on completeness, consistency, and automated verification of functional requirements for the new system. In this paper, we present some guidelines for the integration of early and late requirements specifications. For the organizational modeling we use the i* technique, which focuses on the de- scription of organizational relationships among various organizational actors, as well as an understanding of the rationale for the alternatives chosen. For the functional requirements specification, we rely on the precise Unified Mod- eling Language (pUML), annotated with constraints described in OCL. A small CD store example is used to illustrate how the requirements process it- erates between the early and late requirements specification.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".