Applying empirical software engineering to software architecture: challenges and lessons learned
Bibliographic record
Abstract
In the last 15 years, software architecture has emerged as an important field of software engineering for managing the development and maintenance of large, software-intensive systems. The software architecture community has developed numerous methods, techniques, and tools to support the architecture process. Historically, these advances in software architecture have been mainly driven by talented people and industrial experiences, but there is now a growing need to systematically gather empirical evidence rather than just rely on anecdotes or rhetoric to promote the use of a particular method or tool. The aim of this paper is to promote and facilitate the application of the empirical paradigm to software architecture. To this end, we describe the challenges and lessons learned that we experienced for assessing software architecture research by applying controlled experiments, replicas, expert opinion, systematic literature reviews, observation studies, and surveys. In turn, this should support the emergence of a body of knowledge consisting of more widely-accepted and well-formed theories on software architecture.
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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 | 0.178 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".