From Early Requirements Modeled by the i* Technique to Later Requirements Modeled in Precise UML
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 it