Large Language Models for Software Engineering Diagrams: A Systematic Review of UML and ER modelling

Empirical Studies on UML Sequence Diagrams in Education: A Systematic Literature Review

A systematic literature review of empirical studies on UML SDs in computing education, with a focus on notation, consistency, completeness, and model quality shows that research on comprehension represents the primary focus of literature, followed by studies addressing model quality, while relatively few studies focus on methods and tools.

A Comparative Study of LLMs and Human Judgment in UML Diagram Evaluation

This paper investigates the reliability of LLMs in evaluating UML diagrams generated through reverse engineering processes (source code) and asks: do LLM assessments align with those of human experts?

Automated Business Process Model Generation With Large Language Models: A Systematic Literature Review

The automated generation of business process models from natural language descriptions has recently attracted growing attention at the intersection of Business Process Management (BPM), Natural Language Processing (NLP), and Large Language Models (LLMs). This paper presents a Systematic Literature Review (SLR) on the current state of research in this emerging field. Following the guidelines of Kitchenham et al. and the PRISMA framework, 29 studies published between January 1st, 2023 and March 10th, 2026 were identified, selected, and analyzed. The review addresses two research questions focusing on the applied methodological approaches, the used LLMs, the employed modeling languages, as well as the evaluation strategies, challenges, and limitations reported in the literature. The results show a clear shift from traditional NLP-based techniques toward LLM-only and hybrid approaches. OpenAI’s GPT family, especially GPT-4 and its variants, dominates the field, while BPMN is by far the most frequently used target process modeling language. Furthermore, existing studies evaluate automated process model generation primarily through output-focused methods, such as quantitative metrics, expert reviews, and comparisons with alternative or human-created process models. At the same time, the reviewed studies reveal important challenges, including the continued need for human involvement and the output quality. Overall, current approaches show strong potential, but they still act more as intelligent assistants than as fully autonomous process modelers.

Large Language Models for Requirements Engineering: A Cross-Task Empirical Evaluation

This work presents the first cross-task empirical evaluation of LLMs spanning five RE-related activities, as well as replication materials supporting reproducibility, and a broader understanding of the capabilities, limitations, and practical readiness of current LLMs for RE.

June 2026: Top 10 Cited Articles in Software Engineering & Applications

Analyzing structural and semantic similarities between formal business process models using ChatGPT-5.1: a test report

The results show that the applied LLM can reliably detect structural and semantic differences between formal business process models using Business Process Model and Notation, while distinguishing them from acceptable variations, demonstrating strong potential for automated model validation.