mobile development

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 addresses the lack of empirical evidence on the real-world impact of Continuous Integration (CI) in mobile application development, particularly concerning app store performance. Leveraging a large-scale dataset of open-source Android projects, we systematically compare CI adopters and non-adopters through time-series analysis to assess CI’s effects on development activity, bug-fixing efficiency, release frequency, and user engagement metrics on Google Play—specifically downloads and review counts. Our findings reveal, for the first time, distinct adoption patterns of CI in the mobile ecosystem: CI is predominantly adopted by larger, more active projects in finance and productivity categories, which exhibit higher release frequencies and significantly greater downloads and reviews, while maintaining stable ratings. This work fills a critical gap in empirical research on CI effectiveness within mobile development contexts.

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.

Existing mobile agents are constrained by the graphical user interface (GUI) paradigm, struggling to efficiently handle complex tasks such as batch operations and cross-application workflows. This work presents the first systematic exploration of command-line interfaces (CLIs) as an alternative interaction paradigm, enabling direct invocation of device services and data without requiring screen perception or touch-based actions. We introduce CLI-Advantage, a benchmark suite encompassing five representative scenarios challenging for GUI-based approaches, along with open-sourced evaluation infrastructure. Experimental results demonstrate that, without any mobile-specific fine-tuning, general-purpose code large language models—such as Claude Code—combined with Android CLI toolchains achieve success rates of 71.8% on AndroidWorld and 51.9% on MobileWorld, substantially outperforming current GUI-based baselines. Moreover, these CLI-driven agents accomplish tasks in an average of 10.7 steps, significantly fewer than the 18.6 steps required by GUI counterparts.

Deploying lightweight large language models (LLMs) such as Gemini Nano and LLaMA2-7B on commercial smartphones for privacy-sensitive, on-device inference remains challenging due to hardware-system bottlenecks under real-world constraints. Method: We conduct a systematic, multi-dimensional empirical evaluation across user-centric metrics (token throughput, time-to-first-token, power consumption), system resource utilization (memory bandwidth, GPU/NPU occupancy), and hardware-level controls (DVFS policies), benchmarking mainstream inference engines—including llama.cpp and MLC-LLM—on diverse mobile SoCs (Snapdragon, Dimensity, Apple A-series). Contribution/Results: This work is the first to identify and characterize hardware-system co-bottlenecks induced by LLM workloads on modern mobile SoCs, establishing memory bandwidth and energy efficiency as the primary limiting factors. Our analysis provides empirically grounded insights and actionable optimization pathways for on-device model compression, inference engine design, and AI-accelerator architecture development.

Dynamic analysis of Android applications at the application layer has long been constrained by reliance on physical devices, suffering from poor scalability and limited reproducibility. This work proposes a systematic rehosting approach that migrates Android framework components and preinstalled vendor binaries from real-world firmware into a fully emulated environment. By employing tailored extraction and injection strategies, these components are seamlessly integrated into the AOSP build system to produce bootable emulator images that preserve system integrity and runtime compatibility. The method enables, for the first time, large-scale rehosting of vendor-customized Android firmware in QEMU across multiple SDK versions (31–33). Evaluation on 184 firmware samples demonstrates high success rates in both image construction and booting, with only a few failures attributable to missing dependencies or emulator limitations, thereby validating the feasibility and effectiveness of this approach for scalable and reproducible dynamic analysis.

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 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 scarcity of large-scale, reproducible, fine-grained data on third-party SDK dependencies in mobile applications, which hinders research into technical ecosystems and privacy infrastructures. The authors construct a public dataset comprising 334,719 app-version observations by combining static APK analysis, code-signing matching, and an automated processing pipeline, leveraging AndroZoo and Exodus Privacy rules to achieve code-level SDK identification. Covering nearly 100,000 distinct applications and 246 SDKs, the dataset enables the construction of an app–SDK bipartite network and maps SDKs to their operating companies, thereby revealing upstream technological control structures. This resource provides a reusable infrastructure for empirical studies on third-party dependencies and privacy practices in the Android ecosystem.

This study addresses the limited cross-application generalization of mobile GUI agents and the inadequacy of existing benchmarks in evaluating this deficiency. To this end, it proposes AnyAppBench, a real-time Android benchmark spanning 52 applications that assesses agent performance across heterogeneous interfaces through category control and fixed-target testing. By integrating VLM-as-a-judge evaluation, automated task template generation, and human-in-the-loop annotation, the work establishes a systematic failure taxonomy. The contributions include quantitatively revealing cross-application generalization gaps among thirteen agents, demonstrating that success on source applications is difficult to transfer, and showing that subgoal decomposition strategies yield limited effectiveness. These findings provide critical analytical foundations for improving the robust deployment of mobile GUI agents.