Generating natural language specifications from UML class diagrams

Early phases of software development are known to be problematic, difficult to manage and errors occurring during these phases are expensive to correct. Many systems have been developed to aid the transition from informal Natural Language requirements to semistructured or formal specifications. Furthermore, consistency checking is seen by many software engineers as the solution to reduce the number of errors occurring during the software development life cycle and allow early verification and validation of software systems. However, this is confined to the models developed during analysis and design and fails to include the early Natural Language requirements. This excludes proper user involvement and creates a gap between the original requirements and the updated and modified models and implementations of the system. To improve this process, we propose a system that generates Natural Language specifications from UML class diagrams. We first investigate the variation of the input language used in naming the components of a class diagram based on the study of a large number of examples from the literature and then develop rules for removing ambiguities in the subset of Natural Language used within UML. We use WordNet,a linguistic ontology, to disambiguate the lexical structures of the UML string names and generate semantically sound sentences. Our system is developed in Java and is tested on an independent though academic case study

Visualisation of User Stories to UML Model: A Systematic Literature Review

The usage of Agile methodology in software development project is growing rapidly among industry professionals and academia. Unified Modelling Language (UML) conventionally accompanied Agile software development to model the software requirements. The user story is fundamental and should be identified to communicate the basic requirements between the development team and the stakeholders before the UML model such as the use case diagram, class diagrams, and many others can be designed. However, there are several challenges associated with this process such as poorly organised user stories, natural language complexity, and high time consumption to create them. A systematic literature review (SLR) is conducted to grasp more knowledge about the utilisation of Natural Language Processing (NLP) for UML model generation. A total of 198 papers were initially found in four online databases which are Scopus, IEEE Xplore, ScienceDirect, and ACM Digital Library from the year 2018 until 2022. After removing duplicates, applying inclusion and exclusion criteria, and conducting the full-text assessment, only 20 papers are included as the primary studies. The primary studies are reviewed to discover several important pieces of information which are the challenges of designing UML models, NLP tools and techniques used to generate UML models, UML models generated, and validation methods used for measuring the accuracy of generated models. Finally, this study discusses important elements related to the UML model generation with the utilisation of NLP tools and technique

An Institutional Framework for Heterogeneous Formal Development in UML

We present a framework for formal software development with UML. In contrast to previous approaches that equip UML with a formal semantics, we follow an institution based heterogeneous approach. This can express suitable formal semantics of the different UML diagram types directly, without the need to map everything to one specific formalism (let it be first-order logic or graph grammars). We show how different aspects of the formal development process can be coherently formalised, ranging from requirements over design and Hoare-style conditions on code to the implementation itself. The framework can be used to verify consistency of different UML diagrams both horizontally (e.g., consistency among various requirements) as well as vertically (e.g., correctness of design or implementation w.r.t. the requirements)