Distributed Objects and Software Application Wrappers: A Vehicle for Software Re-engineering
Bibliographic record
Abstract
It is always difficult to ensure the success of a re-engineering project. It takes careful planning to set the objectives and pursue realistic solutions that can be both technically feasible, and have a high benefit to investment ratio. A fundamental requirement for software re-engineering is to ?understand? what and how the existing system delivers its functionality. These tasks can be addressed by re-documentation and design recovery techniques. However, it is not always necessary to re-engineer a system from ground up, and by understanding all of its implementation details. It is a common a scenario in industry, to move towards a software evolutionary pattern in which a legacy system need to be migrated and used in a new operating environment, or be integrated as a component of a new application. Some refer to this pattern as continuous engineering. The requirement in which a re-engineering project is based on the re-use of existing host applications and data with minimal rewriting is too common to be ignored. Within this framework, architectural design recovery of a system, with respect to its major components and its major interfaces, offers a gateway to making legacy system components available to other applications. Distributed Object Technology hides implementation details of these components and provides a vehicle that exposes public interfaces for the legacy system.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".