The Software Architecture Mapping Framework for Managing Architectural Knowledge
Bibliographic record
Abstract
Within a software architecture design (SAD) project, designers deal with software design artifacts (SDAs) such as scenarios, patterns, and tactics.Each SDA has its unique issues and related architectural knowledge (AK) that may threaten the success of a project.This paper introduces the Software Architecture Mapping (SAM) framework to manage AK and associated issues by using finer-grained SDAs and networks of weighted arguments.These networks of data may be used to produce quantitative information in multi-dimensional views to facilitate the identification of critical SDAs and issues in a project.This paper illustrates how the SAM framework has been used to manage AK related to the template method (TM) design pattern in the context of an academic case study.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".