MoDRE 2025: 15th International Model-Driven Requirements Engineering Workshop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Welcome to the 15th International Workshop on Model-Driven Requirements Engineering (MoDRE’25), held in conjunction with the 33rd edition of the Requirements Engineering Conference. The MoDRE workshop series has established a forum where researchers and practitioners can discuss the challenges and opportunities of Model-Driven Development (MDD) for Requirements Engineering (RE).Model-Driven (software) development languages, tools, and techniques have raised the level of abstraction in software development and enabled automation of various parts of the software development process. When effectively applied, MDD techniques can offer significant benefits to RE, by balancing the flexibility for capturing varied user needs with the formality required for model transformations, and by bridging high-level abstraction with the richness of requirements information. MoDRE seeks to explore areas of RE that are not yet fully formalized to be incorporated into an MDD environment. It also seeks to explore how RE models can benefit from advances in the model-driven community, such as flexible, collaborative, and AI-enabled modeling. MoDRE encourages researchers to explore these benefits by identifying new challenges, sharing ongoing work and emerging solutions, analyzing strengths and weaknesses of MDD approaches for RE, and fostering stimulating discussions on the topic during the workshop. This workshop is an opportunity to reflect on the current state and envision the future of MDD approaches for RE.We would like to thank the Program Committee for their valuable feedback to the authors, and, of course, the authors for submitting their papers and making this workshop possible. We are looking forward to an interactive and engaging workshop!
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.
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 | 0.002 | 0.001 |
| 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 it