Model Driven Evolution of Network-Centric Applications: Perspectives, Challenges, and Issues
Bibliographic record
Abstract
Model-driven techniques have been proposed and promoted by the Software Engineering community over the past few years as a mechanism for streamlining the design, implementation and evolution of large software applications. The basic idea behind model-driven techniques is that, design artifacts of large software applications can be represented as a collection of models which can be consequently transformed and evolved to generate specific design artifacts and even source code that complies with specific programmatic paradigms and patterns. Even though model-driven frameworks have caught the attention of the software engineering community as a way to increase programmers' productivity and overall system robustness through the disciplined manipulation and transformation of models and ultimately code generation, they have remained so far only in the form of "guidelines" or "standard practices". In this respect, important questions regarding to what types of models are required for system representation, how transformations are encoded and enacted, how model constraints are denoted and validated, and how source code is generated, is left to software vendors, software architects and software developers to further design and implement. In this keynote presentation we will focus on the challenges, issues, emerging research topics and practical examples pertaining to the use of model-driven techniques for the design, analysis and evolution of network-centric, web-based applications. Some of these challenges in such systems include the use of multi-language paradigms, the problem of maintaining consistency between various models during system evolution, dealing with underlying technology changes, and facilitating end-product customizability.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".