An Orchestrated Multi-view Software Architecture Reconstruction Environment
Bibliographic record
Abstract
Most approaches in reverse engineering literature generate a single view of a software system that restricts the scope of the reconstruction process. We propose an orchestrated set of techniques and a multi-view toolkit to reconstruct three views of a software system such as design, behavior, and structure. Scenarios are central in generating design and behavior views. The design view is reconstructed by transforming a number of scenarios into design diagrams using a novel scenario schema and generating an objectbase of actors and actions and their dependencies. The behavior view is represented by different sets of functions that implement different features of the software system corresponding to a set of feature-specific scenarios that are derived from the design view. Finally, the structure view is reconstructed using modules and interconnections that are resulted by growing the core functions related to the software features that are extracted during the behavior recovery. This orchestrated view reconstruction technique provides a more accurate and comprehensive means for reverse engineering of a software system than a single view reconstruction approach. As case studies we applied the proposed multi-view approach on two systems, Xfig drawing tool and Pine email system
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".