Decision-making techniques for software architecture design
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
The architecture of a software-intensive system can be defined as the set of relevant design decisions that affect the qualities of the overall system functionality; therefore, architectural decisions are eventually crucial to the success of a software project. The software engineering literature describes several techniques to choose among architectural alternatives, but it gives no clear guidance on which technique is more suitable than another, and in which circumstances. As such, there is no systematic way for software engineers to choose among decision-making techniques for resolving tradeoffs in architecture design. In this article, we provide a comparison of existing decision-making techniques, aimed to guide architects in their selection. The results show that there is no “best” decision-making technique; however, some techniques are more susceptible to specific difficulties. Hence architects should choose a decision-making technique based on the difficulties that they wish to avoid. This article represents a first attempt to reason on meta-decision-making, that is, the issue of deciding how to decide.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 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