A Framework of Tools for Managing Software Architecture Knowledge
Bibliographic record
Abstract
Software architecture (SA) process consists of several activities, which involve complex knowledge intensive process. The knowledge produced and consumed during this process needs to be shared and reused among different stakeholders, and across different life-cycle phases. Therefore, software architecture knowledge needs to be managed for improving organization architecture capabilities. It is the way knowledge management (KM) plays an important role in the SA process. This paper utilized SA evaluation to analyze SA and used Architecture Tradeoff Analysis Method (ATAM) to support a disciplined architecture process. With this approach, it gives support to provide or manage the knowledge required or generated during the SA process. The effective tool support is needed and become important to capture and manage architectural knowledge (AK) consumed or generated during SA process. If not captured and managed, this critical knowledge is implicitly embedded in the architecture, become tacit knowledge which erodes as personnel on the project change. To cover these issues, this paper developed a framework of tools for managing SA knowledge. The tool prototype designing and implementing a web-based knowledge management system (KMS), which is offer a hybrid architectural KM approach.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".