TAMVE: Properties of Design Technologies to Address Challenges to Software Design in the Era of Agility and Frameworks
Bibliographic record
Abstract
It has been over 20 years since software design started the transition from the era of big design documents to the era where agility and the use of sophisticated frameworks has become standard. Design in both eras faced challenges: Big documents are rarely maintained properly; agility results in design either being skipped, lost, or dispersed into multiple small files; frameworks such as Ruby on Rails tend to impose a design on the system, encouraging developers to jump into coding. In this position paper, we suggest how design technologies and notations should have five properties that would help overcome many of the challenges to design in the current era. They should be Textual, Analysable, Multi-technology, Visualizable and Example-rich; we use the acronym TAMVE as a mnemonic for this. We explain how the Umple technology goes a considerable distance towards this achieving the TAMVE vision.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".