A design for evidence - based soft research
Bibliographic record
Abstract
Active research is being done in how to go from requirements to architecture. However, no studies have been attempted in this area despite a long history of empirical research in software engineering (SE). Our goal is to establish a framework for the transformation from requirements to architecture on the basis of a series of empirical studies. The first step is to collect evidence about practice in industry before designing relevant techniques, methods and tools. As part of this step, we use an interview-based multiple-case study with a carefully designed process of conducting the interviews and of preparing the data collected for analysis while preserving its integrity. In this paper, we describe the design of this multiple-case study, delineate the evidence trail, discuss validity issues, outline the data analysis focus, discuss meta issues on evidence-based SE particularly on combining and using evidence, describe triangulation approaches, and present two methods for accumulating evidence.
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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 | 0.272 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".