Using MILOS for dependency management in UML-based SE-processes
Bibliographic record
Abstract
The Unified Modeling Language (UML) plays an important role in software engineering. Several life-cycle process models that utilize the UML have been proposed, supported by a variety of development tools Usually, these provide just a little help for the management of the software project itself. This can be accomplished by using the MILOS system that integrates process modeling, project planning, and project enactment technologies for generic processes. So far, the flexible workflow engine allows refining and changing process models during project execution but treats specific products like UML documents as "black-box". The work presented here results from the observation that products, processes, and specific roles within a process should not be considered independently. We propose that the combination of two flexible technologies like MILOS and UML, together with the ability for appropriate tailoring is especially useful in highly dynamic domains like e-business engineering. Therefore, we present an approach that integrates MILOS and the UML in a way that a project manager can benefit from the change management capabilities of MILOS.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".