Agility and Architecture: Can They Coexist?
Bibliographic record
Abstract
Agile development has significantly impacted industrial software development practices. However, despite its wide popularity, there's an increasing perplexity about software architecture's role and importance in agile approaches. Advocates of architecture's vital role in achieving quality goals for large software-intensive systems doubt the scalability of any development approach that doesn't pay sufficient attention to architecture. This article talks about software architecture being relevant to the basis of aspects such as communication among team members, inputs to subsequent design decisions, documenting design assumptions, and evaluating design alternatives. In a large software organization, implementing agile approaches isn't a straightforward adoption problem. Most likely, it will take several years to shorten the feedback cycles to benefit from the adaptability and earlier value-creation opportunities. Failure is a natural part of process improvement.
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.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.020 | 0.044 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".