Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this article proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e., labeled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this article: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks, including GUI classification and sketch-to-GUI retrieval with encouraging results.

Over the past decade, app store (AppStore)-inspired requirements elicitation has proven to be highly beneficial. Developers often explore competitors' apps to gather inspiration for new features. With the advance of Generative AI, recent studies have demonstrated the potential of large language model (LLM)-inspired requirements elicitation. LLMs can assist in this process by providing inspiration for new feature ideas. While both approaches are gaining popularity in practice, there is a lack of insight into their differences. We report on a comparative study between AppStore- and LLM-based approaches for refining features into sub-features. By manually analyzing 1,200 sub-features recommended from both approaches, we identified their benefits, challenges, and key differences. While both approaches recommend highly relevant sub-features with clear descriptions, LLMs seem more powerful particularly concerning novel unseen app scopes. Moreover, some recommended features are imaginary with unclear feasibility, which suggests the importance of a human-analyst in the elicitation loop.

App reviews from app stores are crucial for improving software requirements. A large number of valuable reviews are continually being posted, describing software problems and expected features. Effectively utilizing user reviews necessitates the extraction of relevant information, as well as their subsequent summarization. Due to the substantial volume of user reviews, manual analysis is arduous. Various approaches based on natural language processing (NLP) have been proposed for automatic user review mining. However, the majority of them requires a manually crafted dataset to train their models, which limits their usage in real-world scenarios. In this work, we propose Mini-BAR, a tool that integrates large language models (LLMs) to perform zero-shot mining of user reviews in both English and French. Specifically, Mini-BAR is designed to (i) classify the user reviews, (ii) cluster similar reviews together, (iii) generate an abstractive summary for each cluster and (iv) rank the user review clusters. To evaluate the performance of Mini-BAR, we created a dataset containing 6,000 English and 6,000 French annotated user reviews and conducted extensive experiments. Preliminary results demonstrate the effectiveness and efficiency of Mini-BAR in requirement engineering by analyzing bilingual app reviews.

GUI (graphical user interface) prototyping is a widely-used technique in requirements engineering for gathering and refining requirements, reducing development risks and increasing stakeholder engagement. However, GUI prototyping can be a time-consuming and costly process. In recent years, deep learning models such as Stable Diffusion have emerged as a powerful text-to-image tool capable of generating detailed images based on text prompts. In this paper, we propose UI-Diffuser, an approach that leverages Stable Diffusion to generate mobile UIs through simple textual descriptions and UI components. Preliminary results show that UI-Diffuser provides an efficient and cost-effective way to generate mobile GUI designs while reducing the need for extensive prototyping efforts. This approach has the potential to significantly improve the speed and efficiency of GUI prototyping in requirements engineering.

Abstract Formal methods are proven techniques that provide a rigorous mathematical basis to software development. In particular, they allow the quality of development to be effectively improved by making accurate and explicit modelling, so that anomalies like ambiguities and incompleteness are identified in the early phases of the software development process. Semi‐formal UML models and formal Timed Automata models are used to design a telerehabilitation system through a practical approach based on abstraction and refinement. The formal verification of expected properties of the system is performed by the Uppaal tool. The motivation of this work is threefold: (i) showing the usefulness of formal methods to satisfy the validation needs of a medical telerehabilitation system; (ii) demonstrating our approach of system analysis through refinements to guide the development of a complex system; and (iii) highlighting, from a real‐life experience, the usefulness of models to involve the stakeholders all along the design of a system, from requirements to detailed specifications.

IDF is an Incremental Development Framework which supports the development and the verification of UML models for reactive systems.IDF offers refinement and extension techniques allowing liveness properties to be preserved during the model developments.Here, we improve the framework in order to analyze models from a safety point of view.For this purpose, we associate IDF with the experienced tools of safety analysis based on the BIP language by translating UML models into BIP.We demonstrate on a basic example the complementarity of liveness and safety analyses.

This paper deals with a new approach for the Automatic Test Pattern Generation (ATPG) of circuits described from a behavioral point of view in VHDL. This approach is based on a pseudo-random process characterized by the fact that criteria for computing the test length and evaluating the quality of the generated data come from the field of software engineering. This paper presents the bases of this new approach in the field of hardware engineering and some experimental results.

This paper presents an approach aiming at a significant acceleration of a Behavioral Test Pattern Generation process. Recently, new BTPG methods have been developed, based upon the application of a backtracking search procedure. The approach presented herein reduces the number of backtracks by firstly comparing search strategies and then by defining an improvement criteria.