design mobile app architectures

本文通过对比五个植物管理应用实现（iOS、Android原生及Flutter等跨平台框架），评估了软件质量的权衡，使用ISO/IEC 25010标准衡量不同维度性能。

This study investigates dataset usage patterns in empirical mobile application requirements engineering (RE) research to identify data source bias and its implications for external validity. Following Kitchenham et al.’s systematic mapping methodology, we analyze 43 empirical studies published between 2012 and 2023. Results reveal that over 90% rely exclusively on Google Play and Apple App Store—neglecting critical RE activities such as requirement validation and evolution—while exhibiting pronounced dataset homogeneity despite growing adoption. This work provides the first quantitative evidence of data source narrowing in mobile RE research. To mitigate this risk, we propose a “multi-source data fusion” framework advocating integration across platforms (e.g., F-Droid, GitHub), modalities (e.g., user reviews, source code, changelogs), and RE activities. The framework advances methodological rigor and practical relevance, supporting more generalizable and empirically grounded mobile RE research.

This work addresses the common neglect of software design principles in existing automated code generation approaches, which often results in mobile applications with poor architectural quality. To overcome this limitation, the authors propose a novel method that integrates software product line engineering with variability modeling of design patterns. For the first time, the Universal Variability Language (UVL) is employed to explicitly capture structural and behavioral variations of design patterns, enabling their integration as configurable assets within the code generation pipeline. Leveraging UVL models, the Jinja templating engine, and Swift-based code synthesis, the proposed system supports the customizable, automated generation of design patterns such as Singleton and Strategy. This approach not only preserves architectural integrity but also significantly enhances application maintainability and reusability.

该研究通过引入MobileForge基准，解决了多屏移动应用生成中的跨页导航、代码可维护性等问题，评估了现有模型在项目级生成中的表现。

Traditional GUI testing—relying on source code or layout files—suffers from poor cross-platform compatibility and low robustness due to semantic and structural mismatches across platforms. To address this, this work conducts a systematic literature review of 271 papers and presents the first large-scale taxonomy of 92 vision-driven GUI testing approaches. We propose an end-to-end automated testing framework integrating computer vision techniques—including OCR, object detection, image similarity matching, UI element localization, and semantic understanding. Our analysis reveals paradigmatic advantages in real-world interface adaptation, cross-platform interoperability, and runtime robustness. We identify six core technical challenges and five emerging research directions (e.g., multimodal testing, AIGC-enhanced testing). Furthermore, we construct the first structured knowledge graph for vision-based GUI testing and deliver a practical, actionable research roadmap grounded in empirical findings and technological trends.

This study addresses the challenges of user attrition and limited experimental flexibility in native application rewrites by proposing a Strangler Fig pattern based on a dual-launch mechanism for native platforms. This approach hosts multiple version variants within a single binary, enabling dynamic runtime selection and full lifecycle management through symbol resolution mapping and linker retention lists. As the first application of this pattern to native mobile environments, it facilitates binary-level A/B testing and seamless legacy deprecation. Empirical results from an iOS application rewrite demonstrate that the migration was completed within ten months with zero user churn, significantly enhancing both development efficiency and experimentation capabilities.

This study addresses the unique challenges of B2X mobile application development, where existing process models often fall short and a gap persists between academic research and industry practice. Through a systematic literature review and semi-structured interviews with 28 industry experts, the research identifies key characteristics and core challenges inherent in B2X mobile development contexts. Building on these insights, it proposes the first reference model that integrates both academic rigor and practical relevance, designed to accommodate diverse B2X scenarios. The model emphasizes hybrid development processes and effective communication mechanisms, offering managers a theoretically grounded yet actionable decision-support framework to navigate the complexities of mobile application development.

This study addresses the high development costs and significant code redundancy associated with traditional institutional mobile applications that rely heavily on native Android development. To overcome these limitations, the authors propose a full-stack solution leveraging a Django backend and an HTMX frontend, integrated via a WebView bridge to deliver a campus management system without writing any Android SDK code. The system supports core functionalities including task scheduling, inventory management, and attendance tracking, and is deployed using a self-hosted Docker Compose setup, eliminating dependence on external cloud services. Evaluated in a real-world institutional setting, this approach demonstrates for the first time that HTMX combined with Django can effectively replace conventional APK-based development, achieving a 54% reduction in development time, a 91% decrease in HTTP payload size, and a user satisfaction score of 4.2 out of 5.0 among 42 participants.

This work addresses the challenges posed by the event-driven nature and development diversity of Android applications, which hinder static analysis from constructing complete control-flow models, while existing dynamic testing approaches suffer from low efficiency and insufficient coverage. To overcome these limitations, the paper proposes DroidGraph, a framework that integrates static code analysis with systematic exploration to build a unified control-flow model bridging low-level method invocations and high-level UI structures, thereby enabling efficient automated test generation. Experimental evaluation on 19 real-world applications demonstrates that DroidGraph achieves 18% higher coverage of application content with 345 fewer interactions on average compared to random exploration, and additionally uncovers 51 previously missed components and 49% more UI callback links.

This study addresses the challenges of frequent model switching, energy efficiency, and reconfiguration overhead in autonomous driving scenarios by systematically comparing the deployment trade-offs between overlay architectures and custom accelerators. Through real workload-driven architectural simulations and multidimensional evaluation metrics—including reconfiguration latency, energy efficiency, and flexibility—the work provides the first quantitative assessment of these two architectural paradigms under realistic deployment conditions. The findings reveal that current overlay architectures are better suited for high-frequency switching scenarios; however, as reconfiguration overheads of custom accelerators decrease or the capabilities of overlay systems improve, the optimal deployment strategy may shift. These insights offer critical guidance for future heterogeneous hardware design targeting dynamic autonomous driving workloads.