Proceedings of the 2012 workshop on Next Generation Modularity Approaches for Requirements and Architecture
Bibliographic record
Abstract
As the Internet becomes increasingly pervasive in our daily lives, we are seeing the rise of the Digital World Phenomenon, where the former notions of cyberspace and physical world merge together. This new digital world brings new challenges for software systems and their developers. There is now an open space of services, which are highly adaptive and can be combined in ad-hoc ways to develop complex systems. The complexity of this open space of services is compounded by the fact that increasingly end-user developers are creating and deploying services and applications to be used by third parties. Furthermore, infrastructures such as the Cloud are leading to what has come to be known as Internet-scale applications. New advanced modularisation approaches are needed due to the change in the nature of software systems. Further, the need for distributed application integration requires modularity over multiple language/design approaches, which is substantially different from the traditional modularisation approaches in the single application/single language perspective. This workshop aims to explore whether the current modularity mechanisms to aid modelling and analysis of software system requirements and architectures as sufficient for this changing landscape. If not, what shape should the next generation modularity mechanisms for requirements and architecture take so that they are able to cope with this changing face of software. The workshop aims to take a retrospective look on modularity in requirements and architecture and develop a research agenda for the next 5-10 years.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".