Bibliographic record
Abstract
The Model-Driven Software Development Systems (MDSDS) were initially developed as an attempt to increase software development productivity and quality.This is because focusing on the logical solution abstract is more important than focusing on the pure infrastructure technicalities.Developers discovered the abstracted modelling technique that includes both programming and platform tools in the same time, which is now referred to as MDSDS.Nowadays, there are plenty of modeling software applications that almost achieve the same work, yet, the user might not be aware of the detailed nuances between them.This paper aims to discover the distinguishing features between four of the most commonly used MDSDS including; YAKINDU, Papyrus-RT, Rhapsody, and The State Machine Compiler (SMC).Analysis of the suitability of those platforms for modeling structural and behavioral domain specific software will be investigated.The same model will be built using the four MDSDSs.Then, main differences, obstacles, observations, and overall experience quality using those four environments will be discussed.Some of the common distinguishing features to be explored is GUI intuitivism, user friendliness, clarity of commands and tools, tool learning time needed and learning curve, model building time consumed, etc.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".