Proceedings of the 1st International Workshop on Future of Software Architecture Design Assistants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is our great pleasure and honor to welcome you to the very first International Workshop on the Future of Software Architecture Design Assistants -- FoSADA'15. The idea to hold this workshop was born in the summer of 2014. We have both performed past research in the general field of what we termed Software Architecture Design Assistants, and wish to understand where research in this field stands today, and what might be the path forward. Given the exploratory nature of the Workshop, we decided to aim for position papers. We got four submissions which were all accepted. Two of the papers are concerned with early architectural decisions: In the first paper, Davide Arcelli and Vittorio Cortellessa present a framework aimed at supporting automated feedback generation from software performance analysis results. In the second paper, John Klein and Ian Gorton describe their knowledge base that enables reasoning from quality attributes to architecture patterns and tactics to features implemented in NoSQL products. In the third paper, Sebastian Gerdes, Mohamed Soliman, and Matthias Riebisch consider system evolution and present a decision process focusing on the consideration of constraints in evolving systems. Finally, Sebastian Lehrig and Steffen Becker present a survey of how controlled experiments have been applied to evaluate software architecture design assistants and derive lessons learned in terms of best practices and challenges for such experiments. We look forward to interesting discussions of these papers and general issues at the workshop.
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.
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 it