Developing Semantically-Enabled Families of Method-Oriented Architectures
Bibliographic record
Abstract
Method Engineering (ME) aims to improve software development methods by creating and proposing adaptation frameworks whereby methods are created to provide suitable matches with the requirements of the organization and address project concerns and fit specific situations. Therefore, methods are defined and modularized into components stored in method repositories. The assembly of appropriate methods depends on the particularities of each project, and rapid method construction is inevitable in the reuse and management of existing methods. The ME discipline aims at providing engineering capability for optimizing, reusing, and ensuring flexibility and adaptability of methods; there are three key research challenges which can be observed in the literature: 1) the lack of standards and tooling support for defining, publishing, discovering, and retrieving methods which are only locally used by their providers without been largely adapted by other organizations; 2) dynamic adaptation and assembly of methods with respect to imposed continuous changes or evolutions of the project lifecycle; and 3) variability management in software methods in order to enable rapid and effective construction, assembly and adaptation of existing methods with respect to particular situations. The authors propose semantically-enabled families of method-oriented architecture by applying service-oriented product line engineering principles and employing Semantic Web technologies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".