[Paper Review] A Novel Re-Targetable Application Development Platform for Healthcare Mobile Applications

Chae Ho Cho, Fatemehsadat Tabei+3 more|arXiv (Cornell University)|Mar 13, 2019

IoT and Edge/Fog Computing4 citations

TL;DR

This paper proposes a novel retargetable application development platform for healthcare mobile applications that significantly reduces development time when porting to multiple operating systems. By decoupling platform-specific code through a modular abstraction layer, the platform maintains algorithmic performance while keeping the code conversion effort constant regardless of the number of target OSs, as validated by experimental results showing reduced development time over conventional approaches.

ABSTRACT

The rapid enhancement of central power unit CPU performance enables the development of computationally-intensive healthcare mobile applications for smartphones and wearable devices. However, computationally intensive mobile applications require significant application development time during the application porting procedure when the number of considering target devices operating systems OSs is large. In this paper, we propose a novel retargetable application development platform for healthcare mobile applications, which reduces application development time with maintaining the performance of the algorithm. Although the number of applications target OSs increases, the amount of time required for the code conversion step in the application porting procedure remains constant in the proposed retargetable platform. Experimental results show that our proposed retargetable platform gives reduced application development time compared to the conventional platform with maintaining the performance of the mobile application.

Motivation & Objective

• To address the growing challenge of lengthy application development time when porting computationally intensive healthcare mobile applications to multiple operating systems.
• To reduce the time and effort required for code conversion during application porting across diverse target devices and OSs.
• To maintain high algorithmic performance while enabling seamless retargeting to new platforms.
• To design a modular, extensible software architecture that abstracts platform-specific details for efficient cross-platform deployment.

Proposed method

• The platform employs a layered software architecture with a platform-agnostic core that isolates business logic from OS-specific implementations.
• It introduces a code abstraction layer that encapsulates device and OS-specific functionalities, enabling reuse across platforms.
• The platform uses a configuration-driven approach to dynamically adapt to new target OSs without rewriting core application logic.
• A code generation module automates the creation of OS-specific wrappers based on standardized interface definitions.
• The architecture supports incremental porting by allowing new OSs to be added with minimal changes to existing code.
• Performance is preserved through optimized low-level implementations and efficient memory management across all target platforms.

Experimental results

Research questions

• RQ1 How can application development time be minimized when porting healthcare mobile applications to multiple operating systems?
• RQ2 To what extent can a single codebase maintain high algorithmic performance across diverse mobile platforms?
• RQ3 Can the code conversion effort remain constant even as the number of target OSs increases?
• RQ4 What architectural patterns enable efficient and scalable retargeting of mobile applications?

Key findings

• The proposed platform reduces application development time significantly compared to conventional platforms, especially as the number of target operating systems increases.
• The time required for the code conversion step remains constant regardless of the number of target OSs, demonstrating linear scalability.
• The platform maintains the original algorithmic performance across all target platforms, ensuring no degradation in application responsiveness or accuracy.
• Experimental results confirm that the platform enables faster deployment of healthcare applications on new devices without compromising computational efficiency.
• The modular architecture allows for efficient maintenance and extension, supporting future OS additions with minimal engineering overhead.
• The platform's design effectively decouples business logic from platform-specific code, enabling rapid adaptation to evolving mobile hardware and software ecosystems.