Simultaneous Fault Models for the Generation of Efficient Error Detection Mechanisms

The application of machine learning to software fault injection data has been shown to be an effective approach for the generation of efficient error detection mechanisms (EDMs). However, such approaches to the design of EDMs have invariably adopted a fault model with a single-fault assumption, limiting the practical relevance of the detectors and their evaluation. Software containing more than a single fault is commonplace, with prominent safety standards recognising that critical failures are often the result of unlikely or unforeseen combinations of faults. This paper addresses this shortcoming, demonstrating that it is possible to generate similarly efficient EDMs under more realistic fault models. In particular, it is shown that (i) efficient EDMs can be designed using fault data collected under models accounting for the occurrence of simultaneous faults, (ii) exhaustive fault injection under a simultaneous bit flip model can yield improvements to EDM efficiency, and (iii) exhaustive fault injection under a simultaneous bit flip model can made non-exhaustive, reducing the resource costs of experimentation to practicable levels, without sacrificing resultant EDM efficiency.