In the field of automatic quality or safety assurance level evaluation, this paper proposes the first approach towards the automation of the extraction processes of both the valid and faulty state machines within a System-on-a-Chip. The data automatically extracted by this method is a relevant input for behavioural modelization and FMEA analysis. The method is based on a semi-automated approach for the systematic extraction of failure modes of a digital design in the hypothesis of a singleevent upset (SEU) or stuck-at in flip-flops. This procedure aims to enhance human driven failure analysis and provide inputs for RAMS frameworks in the process of quality assurance of complex devices. The main objective is to transport and apply RAMS methods and tools in the area of SoCs design. Experimental results have been conducted on an I2C -AHB system, laying the base for a complete and more complex analysis on an entire SoC.
In the context of functional verification, the focus has always been on hardware and its ability to be both resilient to errors and to recover from them autonomously. In order to evaluate these characteristics, an extensive use of Fault Injection tools is made to achieve clear and granular results. These testing campaigns are carried out on the entire DUT and require a consistent amount of time and computational resources. The possibility of reducing these costs applying modern techniques as the study of the Dysfunctional State Machine or the proof of concept regarding the composability of single block fault injection campaigns to obtain a library of component of which the reliability metrics are well known, as already been extensively discussed and proven on hardware. In this work instead the application of this methodologies to software is presented for the first time. In order to do so, the software has been divided into basic block, atomic chunks of code having precise carachteristics that will ensure the possibility to study them singularly and then recompose them into a software product which reliability metrics are known, without the need for complete Fault injection campaign.
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