Model-Driven Engineering and Elicitation Techniques: A Systematic Literature Review
Bibliographic record
Abstract
Model-Driven Engineering (MDE) aims to describe a system with the help of a series of models. More abstract models are refined into more concrete models by model transformations that ensure that the information from higher-level models is retained in lower-level models. This reduces the effort required to build lower-level models. The quality of lower-level models is also improved because they are partly based on well-tested model transformations. A key prerequisite for MDE is the definition of formal modeling languages, for which metamodeling is typically used. Formally defined modeling languages capture an ever-growing number of aspects of the software development process from requirements to architecture, design, and implementation details. A commonly held sentiment in the requirements modeling community is that requirements elicitation techniques have received less attention in terms of MDE. We perform a systematic literature review to either refute or confirm this sentiment by exploring the use of metamodels in papers on five well-known elicitation techniques: Cognitive Work Analysis, Complex Adaptive Systems Theory, Contextual Inquiry and Design, Distributed Cognition, and Emergent Knowledge Process design. The result of the survey indicates - within the limitations of the survey - that there is very little research applying metamodeling to the definition of these elicitation techniques.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".