Component Replacement Strategies for Information Systems Reengineering
Bibliographic record
Abstract
Recent trend in systems architecture and design is componentbased.A system is designed as a set of mutually supporting components that provide the intended services.The requirements models such as business type models and use case models are often used for deriving the targeted component-based architecture.The component interfaces are derived via sequence diagrams, collaboration diagrams and context diagrams.As the business model evolves, it becomes vital that the system also needs to match the business evolution whether it involves changing business rule set or growth in volume of business transactions.Timely reengineering of systems is profitable to any organization.The systems reengineering can be conducted in a pragmatic manner via component by component or a selected set of components; it becomes manageable and cost-effective to maintain the system and to train only a smaller sample of affected users.This paper offers a methodology for system reengineering via component replacement and model-viewcontrol framework for component refinement and evolution in order to achieve a reengineered system that reflects upon the latest requirements in business domain.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".