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In the expanding realm of the Internet of Things (IoT), the connectivity of Edge devices is crucial for seamless data exchange. These devices, encompass embedded software operating in real-time, are integral to IoT systems. Functional Size Measurement (FSM), particularly the COSMIC-ISO 19761 method, has become vital for measuring Edge devices’ software functionality in different software development phases. This paper builds on previous research by proposing a novel approach to recursively measure COSMIC Function Points (CFPs) for function calls within Arduino libraries and nested libraries, using regular expressions and caching techniques. This paper explores Arduino libraries, focusing on the implementation of function calls within them. Implemented in Python, this approach aims to provide a comprehensive framework for accurately measuring the functional size of IoT Edge Device code. Through rigorous experimentation, our results demonstrate the effectiveness of our approach.

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COSMIC-REC: A Recursive COSMIC Functional Size Measurement Tool for Arduino IoT Edge Devices

  • 会议论文

  • 发表日期：

    2025年

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  • Sana Abdullah,
  • Salma Salem,
  • Milad Ghantous,
  • Hassan Soubra

摘要

In the expanding realm of the Internet of Things (IoT), the connectivity of Edge devices is crucial for seamless data exchange. These devices, encompass embedded software operating in real-time, are integral to IoT systems. Functional Size Measurement (FSM), particularly the COSMIC-ISO 19761 method, has become vital for measuring Edge devices’ software functionality in different software development phases. This paper builds on previous research by proposing a novel approach to recursively measure COSMIC Function Points (CFPs) for function calls within Arduino libraries and nested libraries, using regular expressions and caching techniques. This paper explores Arduino libraries, focusing on the implementation of function calls within them. Implemented in Python, this approach aims to provide a comprehensive framework for accurately measuring the functional size of IoT Edge Device code. Through rigorous experimentation, our results demonstrate the effectiveness of our approach.