REENGINEERING TECHNIQUE ADAPTATION OF LEGACY SOFTWARE SYSTEMS
Bibliographic record
Abstract
В статье рассматривается адаптация методики реинжиниринга унаследованных систем. Приводится обзор подходов к реинжинирингу. Несмотря на то, что термин «реинжиниринг» в первую очередь относится к изменению бизнес процессов, он удачно подходит и к модернизации программного обеспечения. Обосновывается необходимость адаптации методики. В статье описывается применение адаптированной методики на примере реинжиниринга программного комплекса для прогнозных исследований ТЭК. Приведен исторический обзор версий ПК «ИНТЭК» и описаны поэтапно все шаги проведения его реинжиниринга на основе агентно-сервисного подхода The article presents an adaptation of the legacy systems reengineering technique. An overview of approaches to reengineering is given. Although the term “reengineering” primarily refers to changing business processes, it is well suited to software development. The necessity of adapting the method has been substantiated. The article describes the application of the described methodology on the example of software complex reengineering for predictive research of the fuel and energy complex. A historical overview of the current problem is given and all stages of INTEC PC reengineering are described step by step
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".