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Embedded system architecture-computer embedded software defect prediction based on genetic optimisation algorithms

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  • Aiju Wang

Abstract

With the rapid development of electronic measurement technology, people have put forward higher requirements for the diversity of oscilloscope functions and abundant peripheral interfaces. This paper aims to use genetic optimisation algorithms to detect embedded software defects, provide users with prepared defect information, and improve the efficiency and accuracy of detection. This paper proposes popular algorithms for moving target video detection, selection operator, crossover operator and mutation operator, and establishes a complete system, deepening a virtual simulation environment for embedded software development model. In addition, from hardware simulation to the detection of software defects such as memory leaks and uninitialised variables, they are all included in the system and run through the entire process of embedded software development. The experimental results in this paper show that the complete simulation technology has realised a multi-architecture emulator Emu, combined with the defect detection software Valgrind, has realised a complete lack of phase detection system, and the detection rate is as high as 96.7%.

Suggested Citation

  • Aiju Wang, 2023. "Embedded system architecture-computer embedded software defect prediction based on genetic optimisation algorithms," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 22(3/4), pages 262-280.
    • Handle: RePEc:ids:ijitma:v:22:y:2023:i:3/4:p:262-280
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