Systematic selection of software architecture styles
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
Selecting appropriate styles for software architectures is important as styles impact characteristics of software (e.g. reliability). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how these elements are integrated in the architecture. Therefore this study presents a method, called SYSAS, for the systematic selection of architecture styles. In SYSAS, style selection is based on (a) characteristics of basic architectural elements that are relevant for the developer, and (b) characteristics of the target system that are visible to the end user. The selection procedure requires ratings about the importance of characteristics of architectural elements and results in a ranking of styles. SYSAS can be applied at system level as well as for choosing styles for individual subsystems. A case study is presented to illustrate SYSAS and its applicability and added benefit. Additional case studies are performed to compare results of SYSAS with judgements of experts.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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