Automated Derivation of UML Sequence Diagrams from User Stories: Unleashing the Power of Generative AI vs. a Rule-Based Approach
Bibliographic record
Abstract
User stories are informal, non-technical descriptions of features from a user's perspective that guide collaboration and iterative development in Agile projects. However, ambiguities in user stories can lead to miscommunication among stakeholders. Design models, such as UML sequence diagrams, are essential for enhancing communication, clarifying system behavior, and improving the development process. This paper presents an automated approach for generating behavioral models specifically sequence diagrams from natural language requirements expressed as user stories. We also investigate the effectiveness of a Large Language Model (LLM) in using generative AI for this task. By applying our approach and ChatGPT to two benchmark datasets with the same set of user stories, we generated corresponding sequence diagrams for comparison. Expert evaluations in Software Engineering reveal that our approach effectively produces relevant, simplified diagrams for straightforward user stories, whereas the LLM tends to create more complex diagrams that sometimes go beyond the simplicity of the original user stories.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".