Context: Having domain models derived from textual specifications has proven to be very useful in the early phases of software engineering. However, creating correct domain models and establishing clear links with the textual specification is a challenging task, especially for novice modelers. Objective: We propose an approach for determining the alignment between a partial domain model and a textual specification. Methods: To this aim, we use Natural Language Processing techniques to pre-process the text, generate an artificial natural language specification for each model element, and then use an LLM to compare the generated description with matched sentences from the original specification. Ultimately, our algorithm classifies each model element as either aligned (i.e., correct), misaligned (i.e., incorrect), or unclassified (i.e., insufficient evidence). Furthermore, it outputs the related sentences from the textual specification that provide the evidence for the determined class. Results: We have evaluated our approach on a set of examples from the literature containing diverse domains, each consisting of a textual specification and a reference domain model, as well as on models containing modeling errors that were systematically derived from the correct models through mutation. Our results show that we are able to identify alignments and misalignments with a precision close to 1 and a recall of approximately 78%, with execution times ranging from 18 s to 1 min per model element. Conclusion: Since our algorithm almost never classifies model elements incorrectly, and is able to classify over 3/4 of the model elements, it could be integrated into a modeling tool to provide positive feedback or generate warnings, or employed for offline validation and quality assessment.

In domain modelling, practitioners manually transform informal requirements written in natural language (problem descriptions) to more concise and analyzable domain models expressed with class diagrams. With automated domain modelling support using existing approaches, manual modifications may still be required in extracted domain models and problem descriptions to make them more accurate and concise. For example, educators teaching software engineering courses at universities usually use an incremental approach to build modelling exercises to restrict students in using intended modelling patterns. These modifications result in the evolution of domain modelling exercises over time. To assist practitioners in this evolution, a synergy between interactive support and automated domain modelling is required. In this paper, we propose a bot-assisted approach to allow practitioners perform domain modelling quickly and interactively. Furthermore, we provide an incremental learning strategy empowered by machine learning to improve the accuracy of the bot's suggestions and extracted domain models by analyzing practitioners' decisions over time. We evaluate the performance of our bot using test problem descriptions which shows that practitioners can expect to get useful support from the bot when applied to exercises of similar size and complexity, with precision, recall, and F2 scores over 85%. Finally, we evaluate our incremental learning strategy where we observe a reduction in the required manual modifications by 70% and an improvement of F2 scores of extracted domain models by 4.2% when using our proposed approach and learning strategy together.

Domain modelling abstracts real-world entities and their relationships in the form of class diagrams for a given domain problem space. Modellers often perform domain modelling to reduce the gap between understanding the problem description which expresses requirements in natural language and the concise interpretation of these requirements. However, the manual practice of domain modelling is both time-consuming and error-prone. These issues are further aggravated when problem descriptions are long, which makes it hard to trace modelling decisions from domain models to problem descriptions or vice-versa leading to completeness and conciseness issues. Automated support for tracing domain modelling decisions in both directions is thus advantageous. In this paper, we propose an automated approach that uses artificial intelligence techniques to extract domain models along with their trace links. We present a traceability information model to enable traceability of modelling decisions in both directions and provide its proof-of-concept in the form of a tool. The evaluation on a set of unseen problem descriptions shows that our approach is promising with an overall median F2 score of 82.04%. We conduct an exploratory user study to assess the benefits and limitations of our approach and present the lessons learned from this study.

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.

Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges.

Collaborative modelling has become a necessity when developing a complex system or in a team of modellers with a diverse set of expertise. Textual notations have a long history in software engineering because of their fast editing style, simple usage, and scalability. Therefore, we propose a novel collaborative modelling framework for the graphical User Requirements Notation (URN) which we call tColab. It uses the text-based TGRL (Textual Goal-oriented Requirement Language) to build URN goal models and then automatically generates corresponding graphical models. This framework is based on the architecture of Eclipse Che and Theia. On one side, Theia provides support for LSP (Language Server Protocol) so that textual models can be built and their corresponding graphical models can be generated in a browser IDE (Integrated Development Environment). On the other hand, Eclipse Che adds support for collaboration where multiple modellers can contribute to building the textual models in an online collaborative manner. This initiative aims to replace the jUCMNAV tool, which is the most comprehensive URN modelling tool to date but only supports a single user.