native 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 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.

Android app builds frequently fail due to complex dependency graphs, heterogeneous configuration schemes, and rapid ecosystem evolution. To address this, we conduct an empirical study analyzing build logs from 200 open-source Android projects and propose a five-stage diagnosis-and-repair framework. We systematically categorize build failures into four canonical patterns—the first such taxonomy in the literature. Innovatively, we integrate a large language model (GPT-5) to generate context-aware repair suggestions, which are then validated through automated build execution and manual verification. Our evaluation shows successful repair of 102 out of 135 initially failing projects (75.6% success rate). Furthermore, we identify programming language choice, project age, and application scale as statistically significant factors influencing build reliability. This work contributes both a reusable methodology and empirically grounded insights to enhance Android build robustness and maintainability.

GUI test transfer across applications faces challenges such as mapping failure and incomplete test coverage due to structural heterogeneity among UI controls. This paper proposes a novel “abstraction–concretization” paradigm: instead of performing interface-level control mapping, it first abstracts universal test logic from multi-source, functionally equivalent test cases; then, leveraging a large language model (LLM), it concretizes this logic into executable test scripts for the target app via functional semantic alignment and joint GUI event–assertion generation. To our knowledge, this is the first approach to deeply integrate LLMs throughout both test logic generalization and target-specific adaptation, enabling functional-semantic-level transfer. Evaluated on the FrUITeR and Lin datasets, our method achieves functional test success rates of 64% (+191%) and 75% (+42%), respectively—demonstrating substantial improvements in transfer effectiveness and practicality.

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。

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 work addresses the pervasive yet elusive performance issues in mobile applications—such as UI jank and thermal throttling—caused by native libraries compiled with low-level optimization flags (e.g., O0/O1), which are notoriously difficult to detect. To tackle this, we propose OptDetect, the first end-to-end framework capable of identifying mixed optimization levels without requiring source code or build metadata. OptDetect leverages binary disassembly, machine learning–driven block-level optimization classification, and a weighted scoring aggregation scheme to accurately pinpoint under-optimized code segments. Evaluation on 830 popular apps reveals that 91.7% are affected by poorly optimized third-party libraries. Post-remediation results demonstrate up to a 63% reduction in CPU instructions, a median 42% drop in user-reported performance complaints, and a 0.14-point increase in app store ratings, confirming both the efficacy and industry-wide applicability of our approach.

研究提出AppEval，针对移动应用修复问题，通过统一基准和原生工具链评估框架，在ArkTS、Swift和Kotlin中测试修复效果，确保修复能跨越构建-安装-启动-测试边界。

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 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.