A FRAMEWORK FOR TOOL-BASED SOFTWARE ARCHITECTURE RECONSTRUCTION
Bibliographic record
Abstract
For software with nontrivial size and complexity, it is not feasible to manually perform architecture reconstruction. Therefore it is essential for the software architecture miner who is mining architecture from the existing software to have a well-defined software architecture reconstruction process that helps incorporate as much tool use as possible at the appropriate steps of architecture reconstruction. There are some existing software architecture reconstruction frameworks but they do not provide guidelines on how to systematically utilize tools to produce architecture views for a reconstruction purpose. In this paper, we propose a framework for tool-based software architecture reconstruction. This framework consists of a generic process for software architecture reconstruction and the steps to derive from it a concrete tool-based process to be used for actual architecture reconstruction. The architecture miner can use this framework to analyze source code for modifying source code as well as to reconstruct software architecture from source code.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".