The registration of optical and synthetic aperture radar (SAR) images is severely affected by nonlinear radiometric distortions (NRD) and speckle noise. To address these challenges, we propose a novel Multi-scale Phase Asymmetry based Optical SAR registration (MSPA-OS) method, which pioneeringly incorporates phase asymmetry (PA) into the feature extraction process. Compared with phase congruency (PC), PA is more robust to noise. By aggregating PA across multiple scales, we efficiently extract the comprehensive structural features of images. Moreover, a multi-region cross-scale matching (MRCSM) strategy with the rotation-invariant descriptors is devised to handle substantial geometric deformations. Furthermore, MSPA-OS employs a set of monogenic filters to process images, significantly increasing the computational speed. Finally, we compare the performance of MSPA-OS with those of seven state-of-the-art methods using synthetic and real datasets. The experimental results show that MSPA-OS exhibits competitive registration robustness and speed.

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.

Traditional requirements engineering methods typically involve interviews, observations, and questionnaires administered to stakeholders. Our hypothesis posits that this integral process can be significantly accelerated, and even enhanced, through strategic mining of app stores. App stores, such as Google Play and Apple Store, host millions of applications, making it likely to identify apps with features similar to the ones we intend to develop. These apps provide a wealth of information, including app descriptions, app reviews, and user interfaces. However, due to the vast quantity of apps, manual analysis of this information can be labor-intensive. To bridge this gap, our research aims to develop an approach called ReqRec for mining app stores, which will reduce the effort required by requirements engineers during the phase of requirements elicitation. To demonstrate the effectiveness of ReqRec, it will be applied to a case study involving the designing of a health monitoring application.

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.