Towards Requirements-Driven Information Systems Engineering
Bibliographic record
Abstract
Information systems of the future will have to better match their operational organizational environment. Unfortunately, development methodologies have traditionally been inspired by programming concepts, not organizational ones, leading to a semantic gap between the system and its environment. To reduce as much as possible this gap, this paper proposes a development methodology named Tropos which is founded on concepts used to model early requirements. Our proposal adopts the i* organizational modeling framework, which o#ers the notions of actor, goal and (actor) dependency, and uses these as a foundation to model early and late requirements, architectural and detailed design. The methodology complements well proposals for agent-oriented programming platforms, given that the system is defined in term of autonomous, intentional and social software actors. The paper outlines Tropos phases through an e-business example, sketches a formal language which underlies the methodology and is intended to support formal analysis, organizational styles and social (agent) patterns used to design the architecture, UML extensions for detailing design models, and the generation of an implementation within the JACK agent platform. Key words: Development methodology, requirements engineering, agent-oriented software engineering, software architectures. 1 E-mail: jbc@cin.ufpe.br 2 Corresponding author. Tel.: +1-416-978-7569; fax +1-416-978-1455; e-mail: mkolp@cs.toronto.edu 3 E-mail: jm@cs.toronto.edu Preprint submitted to Elsevier Preprint 24 July 2001 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".