Abstract
Context: Testing activities are essential for the quality assurance of mobile applications under development. Despite its importance, some studies show that testing is not widely applied in mobile applications. Some characteristics of mobile devices and a varied market of mobile devices with different operating system versions lead to a highly fragmented mobile ecosystem. Thus, researchers put some effort into proposing different solutions to optimize mobile application testing. Objective: The main goal of this paper is to provide a categorization and classification of existing testing infrastructures to support mobile application testing. Methods: To this aim, the study provides a Systematic Mapping Study of 27 existing primary studies. Results: We present a new classification and categorization of existing types of testing infrastructure, the types of supported devices and operating systems, whether the testing infrastructure is available for usage or experimentation, and supported testing types and applications. Conclusion: Our findings show a need for mobile testing infrastructures that support multiple phases of the testing process. Moreover, we showed a need for testing infrastructure for context-aware applications and support for both emulators and real devices. Finally, we pinpoint the need to make the research available to the community whenever possible.

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This study presents and applies real-time in-vivo measurement and analysis from knee instrumented implant scaling personalized post-operative (PO) recovery process after total knee arthroplasty (TKA) on patients with osteoarthritis. A total of 110 trials were assessed during PO rehabilitation, with acquired data and processing based on the variability from knee joint load normalized to the subject specific body weight pointing to the most adequate gait mode and support for each subject PO and recommendations to inform TKA recovery. © 2025 IEEE.

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The rapid proliferation of Edge AI applications demands efficient, low-power computing architectures tailored to specific workloads. The RISC-V ecosystem is a promising solution, and has led to a fast growth of implementations based on custom instructions extensions, but with varying degrees of functionality and support which may hinder easy adoption. In this paper, we extensively review existing RISC-V extensions targeting primarily the AI domain and respective compilation flows, highlighting challenges in deployment, usability, and compatibility. We further implement and provide usable containerized environments for two of these works. To address the identified challenges, we then propose an approach for lightweight early validation of custom instructions via source-to-source transformations, without need of compiler modifications. We target our own Single Instruction Multiple Data (SIMD) accelerator, which we integrate into a CORE-V cv32e40px baseline core through custom instructions, and versus which we achieve up to 11.9x speedup for matrix-vector operations.

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The Vehicle Routing Problem with Synchronization (VRPSync) aims to minimise the total routing costs while considering synchronization requirements that must be fulfilled between tasks of different routes. These synchronization requirements are especially relevant when it is necessary to have tasks being performed by vehicles within given temporal offsets, a frequent requirement in applications where multiple vehicles, crews, materials, or other resources are involved in certain operations. Although several works in the literature have addressed this problem, mainly the deterministic version has been tackled so far. This paper presents a robust optimization approach for the VRPSync, taking into consideration the uncertainty in vehicle travel times between customers. This work builds on existing approaches in the literature to develop mathematical models for the Robust VRPSync, as well as a branch-and-cut algorithm to solve more difficult problem instances. A set of computational experiments is also devised and presented to obtain insights regarding key performance parameters of the mathematical models and the solution algorithm. The results suggest that solution strategies where certain standard problem constraints are only introduced if a candidate solution violates any of those constraints provide more consistent improvements than approaches that rely on tailor-made cutting planes, added through separation routines. Furthermore, the analysis of the Price of Robustness indicators shows that the adoption of robust solutions can have a significant increase in the total costs, however, this increase quickly plateaus as budgets of uncertainty increase.

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