DoMoBOT: A Modelling Bot for Automated and Traceable Domain Modelling
Bibliographic record
Abstract
In the initial phases of the software development cycle, domain modelling is typically performed to transform informal requirements expressed in natural language into concise and analyzable domain models. These models capture the key concepts of an application domain and their relationships in the form of class diagrams. Building domain models manually is often a time-consuming and labor-intensive task. The current approaches which aim to extract domain models automatically, are inadequate in providing insights into the modelling decisions taken by extractor systems. This inhibits modellers to quickly confirm the completeness and conciseness of extracted domain models. To address these challenges, we present DoMoBOT, a domain modelling bot that uses a traceability knowledge graph to enable traceability of modelling decisions from extracted domain model elements to requirements and vice-versa. In this tool demo paper, we showcase how the implementation and architecture of DoMoBOT facilitate modellers to extract domain models and gain insights into the modelling decisions taken by our bot.
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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 | 0.000 | 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 | 0.000 |
| 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".