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Legacy single-function digital devices (e.g., PDAs, digital cameras, MP3 players) fail to integrate with cloud-centric workflows due to obsolescence and protocol incompatibility. Method: This work proposes a resilience-oriented hardware repurposing paradigm—leveraging protocol reverse engineering, community-driven firmware development, physical interface adaptation, and modular accessory design to enable intermittent connectivity between legacy devices and modern cloud services. Four device classes were successfully refurbished and validated. Results: The approach preserves inherent advantages—including focused interaction, extended battery life, and tactile input—while enabling cloud synchronization, remote control, and bidirectional data transmission. Contributions include: (1) a novel single-task interaction model grounded in “attention-aware hardware + intermittent connectivity”; (2) a set of reusable design principles for resilient hardware; and (3) an open-source toolchain and methodology for sustainable human–computer interaction, supporting ethical hardware longevity and circular design practices.

This study addresses the challenges of unstable end-to-end testing for Android applications in continuous integration (CI) due to fragile emulator configurations. It presents the first large-scale empirical analysis of 4,518 open-source projects, systematically examining how instrumentation tests are configured, how these practices evolve, and their comparative effectiveness in CI environments. Leveraging GitHub Actions metadata, the work evaluates three prevalent approaches: Gradle Managed Devices, community-reusable components, and custom scripts. Findings reveal that only 10.6% of projects adopt such testing; among them, community components demonstrate superior reliability and efficiency, third-party device labs are suitable for regression testing despite higher costs, and custom scripts, while flexible, suffer from high retry rates. The study thus illuminates current practices and critical trade-offs in Android CI testing.

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.

To address the challenge of simultaneously ensuring security, portability, and cloud-native upgradability in hardware access for IoT/edge devices, this paper introduces the first WASI-standard extension for hardware interfaces, enabling sandboxed, memory-safe execution of I²C and USB drivers. Methodologically, it integrates device drivers into the WASI ecosystem by designing a standardized hardware abstraction layer, embedding protocol stacks, and implementing a Rust+WASI SDK. It achieves the first WASI-compliant USB stack, incurring ≤8% performance overhead over native APIs. Key contributions include: (1) establishing a cross-platform, secure, and controllable hardware access paradigm; (2) proposing a WASI hardware specification that balances long-term device compatibility with dynamic runtime updates; and (3) empirically demonstrating the feasibility of direct WebAssembly–hardware interaction, while identifying runtime initialization latency as the critical bottleneck in low-latency scenarios.

Manually coding for IoT device integration faces high expertise barriers and poor cross-platform compatibility. Method: This paper proposes an automated code generation framework for multimodal IoT systems, adopting a “divide-and-conquer” strategy that integrates device-specific knowledge via progressive retrieval, platform-compliance–constrained code synthesis, and LLM-driven iterative refinement. It introduces a novel multi-stage debugging pipeline combining virtual-device-based automated validation with hardware-in-the-loop interactive debugging requiring only binary user feedback—significantly reducing domain expertise requirements. Contribution/Results: Evaluated on 34 real-world IoT devices, the framework achieves a 93.87% average code generation success rate and 94.87% functional coverage, reaching 100% with minimal user feedback. A user study demonstrates that generated code accuracy surpasses that of expert programmers by 50–80%.

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 lack of systematic empirical investigation into the technical and integration challenges of deploying the Matter standard in practice. By analyzing over 13,000 developer issues from the official Project CHIP GitHub repository, this work employs topic modeling and qualitative content analysis to uncover key obstacles encountered during Matter implementation at scale. The research identifies four core challenge categories: testing, interoperability, development support, and platform networking. These findings provide the first community-driven empirical evidence and actionable recommendations for improving Matter’s test infrastructure, cross-vendor documentation, and developer toolchains.

Official Android API lists (AALs) suffer from inconsistencies and instability, severely undermining the reliability and reproducibility of API-based Android research. This study systematically examines discrepancies among four widely used AALs, their version evolution, and real-device availability, complemented by large-scale static analysis of 17,759 applications. For the first time, it uncovers underlying policy divergences and the pervasive use of vendor-customized APIs. The findings reveal that substantial differences across AALs can materially alter research conclusions, while existing studies largely overlook vendor-specific APIs. Based on these insights, the paper offers practical recommendations to improve the reliability of AAL selection and interpretation in future research.

This work addresses key challenges in deploying small language models (SLMs) on mobile devices—including erroneous outputs, constraint violations, contextual degradation, latency incompatibility, and unstable model selection—by integrating Gemma-2.6B and Qwen3-0.6B into the Android word game Palabrita. The authors simplify the LLM’s role from generating full puzzles to producing only three hints and introduce a deterministic fallback mechanism. Embracing a “less is more” principle, they derive eight design heuristics for SLM integration on mobile platforms and systematically categorize five failure modes along with corresponding mitigation strategies. Through techniques such as multi-layer defensive parsing, context-aware retries, conversational turn rotation, and responsibility minimization, the approach enables stable, production-grade SLM deployment, substantially improving system reliability and user experience.

This work addresses the longstanding limitations in chip design education imposed by proprietary EDA tools, PDKs, and IP blocks, which hinder the development of domain-specific SoC engineers with full-stack practical skills. To overcome this barrier, the authors present the first end-to-end open-source SoC tapeout flow for educational use, built around the highly customizable RISC-V platform Croc. The framework integrates open-source SystemVerilog IP, a 130nm open PDK, and a complete open-source EDA toolchain, enabling multi-dimensional customization—including instruction set extensions, coprocessors, and peripherals. In its inaugural deployment, 65 students completed 33 projects, yielding 30 manufacturable layouts and 5 successful tapeouts. The baseline chip has undergone silicon validation, demonstrating functionality and performance comparable to proprietary solutions, thereby significantly lowering the barrier to SoC design and validating the feasibility of a fully open-source design methodology.