Figure 1: GUI prototype (1) and three views (2-4) of our assistant for GUI prototype designers integrated as a plug-in into a prototyping tool.Our assistant displays user stories (2) imported from collaboration tools (e.g., JIRA) for prototype designers to reference while working.It detects whether a user story is implemented (3, 4), identifies relevant GUI components (3), and generates GUI components for user stories (4). Figure uses Google Material 3 Design Kit [24] components under CC BY 4.0.

Graphical User Interface (GUI) prototyping is a fundamental component in the development of modern interactive systems, which are now ubiquitous across diverse application domains. GUI prototypes play a critical role in requirements elicitation by enabling stakeholders to visualize, assess, and refine system concepts collaboratively. Moreover, prototypes serve as effective tools for early testing, iterative evaluation, and validation of design ideas with both end users and development teams. Despite these advantages, the process of constructing GUI prototypes remains resource-intensive and time-consuming, frequently demanding substantial effort and expertise. Recent research has sought to alleviate this burden through natural language (NL)-based GUI retrieval approaches, which typically rely on embedding-based retrieval or tailored ranking models for specific GUI repositories. However, these methods often suffer from limited retrieval performance and struggle to generalize across arbitrary GUI datasets. In this work, we present GUI-ReRank, a novel framework that integrates rapid embedding-based constrained retrieval models with highly effective multi-modal (M)LLM-based reranking techniques. GUI-ReRank further introduces a fully customizable GUI repository annotation and embedding pipeline, enabling users to effortlessly make their own GUI repositories searchable, which allows for rapid discovery of relevant GUIs for inspiration or seamless integration into customized LLM-based retrieval-augmented generation (RAG) workflows. We evaluated our approach on an established NL-based GUI retrieval benchmark, demonstrating that GUI-ReRank significantly outperforms state-of-the-art (SOTA) tailored Learning-to-Rank (LTR) models in both retrieval accuracy and generalizability. Additionally, we conducted a comprehensive cost and efficiency analysis of employing MLLMs for reranking, providing valuable insights regarding the trade-offs between retrieval effectiveness and computational resources. Video presentation of GUI-ReRank available at: https://youtu.be/7x9UCh82ug

Requirements Elicitation (RE) is a crucial activity especially in the early stages of software development. GUI prototyping has widely been adopted as one of the most effective RE techniques for user-facing software systems. However, GUI prototyping requires (i) the availability of experienced requirements analysts, (ii) typically necessitates conducting multiple joint sessions with customers and (iii) creates considerable manual effort. In this work, we propose SERGUI, a novel approach enabling the Self-Elicitation of Requirements (SER) based on an automated GUI prototyping assistant. SERGUI exploits the vast prototyping knowledge embodied in a large-scale GUI repository through Natural Language Requirements (NLR) based GUI retrieval and facilitates fast feedback through GUI prototypes. The GUI retrieval approach is closely integrated with a Large Language Model (LLM) driving the prompting-based recommendation of GUI features for the current GUI prototyping context and thus stimulating the elicitation of additional requirements. We envision SERGUI to be employed in the initial RE phase, creating an initial GUI prototype specification to be used by the analyst as a means for communicating the requirements. To measure the effectiveness of our approach, we conducted a preliminary evaluation. Video presentation of SERGUI at: https://youtu.be/pzAAB9Uht80

Interactive systems are omnipresent today and the need to create graphical user interfaces (GUIs) is just as ubiq-uitous. For the elicitation and validation of requirements, GUI prototyping is a well-known and effective technique, typically employed after gathering initial user requirements represented in natural language (NL) (e.g., in the form of user stories). Un-fortunately, G UI prototyping often requires extensive resources, resulting in a costly and time-consuming process. Despite various easy-to-use prototyping tools in practice, there is often a lack of adequate resources for developing G UI prototypes based on given user requirements. In this work, we present a novel Large Language Model (LLM)-based approach providing assistance for validating the implementation of functional NL- based require-ments in a GUI prototype embedded in a prototyping tool. In particular, our approach aims to detect functional user stories that are not implemented in a G UI prototype and provides recommendations for suitable GUI components directly imple-menting the requirements. We collected requirements for existing GUIs in the form of user stories and evaluated our proposed validation and recommendation approach with this dataset. The obtained results are promising for user story validation and we demonstrate feasibility for the GUI component recommendations.

Abstract Rapid GUI prototyping has evolved into a widely applied technique in early stages of software development to facilitate the clarification and refinement of requirements. Especially high-fidelity GUI prototyping has shown to enable productive discussions with customers and mitigate potential misunderstandings, however, the benefits of applying high-fidelity GUI prototypes are accompanied by the disadvantage of being expensive and time-consuming in development and requiring experience to create. In this work, we showRaWi, a data-driven GUI prototyping approach that effectively retrieves GUIs for reuse from a large-scale semi-automatically created GUI repository for mobile apps on the basis of Natural Language (NL) searches to facilitate GUI prototyping and improve its productivity by leveraging the vast GUI prototyping knowledge embodied in the repository. Retrieved GUIs can directly be reused and adapted in the graphical editor ofRaWi. Moreover, we present a comprehensive evaluation methodology to enable (i) the systematic evaluation of NL-based GUI ranking methods through a novel high-quality gold standard and conduct an in-depth evaluation of traditional IR and state-of-the-art BERT-based models for GUI ranking, and (ii) the assessment of GUI prototyping productivity accompanied by an extensive user study in a practical GUI prototyping environment.

High-fidelity Graphical User Interface (GUI) prototyping is a well-established and suitable method for enabling fruitful discussions, clarification and refinement of requirements formulated by customers. GUI prototypes can help to reduce misunderstandings between customers and developers, which may occur due to the ambiguity comprised in informal Natural Language (NL). However, a disadvantage of employing high-fidelity GUI prototypes is their time-consuming and expensive development. Common GUI prototyping tools are based on combining individual GUI components or manually crafted templates. In this work, we present GUI2WiRe, a tool that enables users to retrieve GUI prototypes from a semiautomatically created large-scale GUI repository for mobile applications matching user requirements specified in Natural Language (NLR). We extract multiple text segments from the GUI hierarchy data and employ various Information Retrieval (IR) models and Automatic Query Expansion (AQE) techniques to achieve ad-hoc GUI retrieval from NLR. Retrieved GUI prototypes mined from applications can be inserted in the graphical editor of GUI2WiRe to rapidly create wireframes. GUI components are extracted automatically from the GUI screenshots and basic editing functionality is provided to the user. Finally, a preview of the application is created from the wireframe to allow interactive exploration of the current design. We evaluated the applied IR and AQE approaches for their effectiveness in terms of GUI retrieval relevance on a manually annotated collection of NLR and discuss our planned user studies. Video presentation of GUI2WiRe: https://youtu.be/2nN-Xr2Hk7I

High-fidelity GUI prototyping provides a meaningful manner for illustrating the developers' understanding of the requirements formulated by the customer and can be used for productive discussions and clarification of requirements and expectations. However, high-fidelity prototypes are time-consuming and expensive to develop. Furthermore, the interpretation of requirements expressed in informal natural language is often error-prone due to ambiguities and misunderstandings. In this dissertation project, we will develop a methodology based on Natural Language Processing (NLP) for supporting GUI prototyping by automatically translating Natural Language Requirements (NLR) into a formal Domain-Specific Language (DSL) describing the GUI and its navigational schema. The generated DSL can be further translated into corresponding target platform prototypes and directly provided to the user for inspection. Most related systems stop after generating artifacts, however, we introduce an intelligent and automatic interaction mechanism that allows users to provide natural language feedback on generated prototypes in an iterative fashion, which accordingly will be translated into respective prototype changes.