Rastreabilidade de Códigos Executáveis usando Redes Neurais

Tiago M. NascimentoLuiz F. R. C. CarmoDavidson R. BoccardoRaphael C. MachadoCharles B. Prado

Traceability of codes refers to the mapping between equivalent codes written in different languages - including high-level and low-level programming languages. In the field of Legal Metrology, it is critical to guarantee that the software binary code embedded in a meter corresponds to a program source code that was previously approved by the Legal Metrology Authority. In this paper, we propose a novel approach for correlating source and binary codes using artificial neural networks. Our approach correlates the source code with the binary code by feeding the neural network with logical flow characteristics of such codes. Any incidence of false positives is obviously a critical issue for software evaluation purposes. Our evaluation using real code examples shows a typical correspondence rate between 62% and 90% for the traceability of the binary codes with the very low rate of 4% false positives.

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