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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 relevance of the detectors and their evaluation. Software containing more than a single fault is commonplace, with safety standards recognizing 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 efficient EDMs under simultaneous fault models. In particular, it is shown that (i) efficient EDMs can be designed using fault injection data collected under models accounting for the occurrence of simultaneous faults, (ii) exhaustive fault injection under a simultaneous bit flip model can yield improved EDM efficiency, (iii) exhaustive fault injection under a simultaneous bit flip model can be made non-exhaustive and (iv) EDMs can be relocated within a software system using program slicing, reducing the resource costs of experimentation to practicable levels without sacrificing EDM efficiency.

Simultaneous Fault Models for the Generation and Location of Efficient Error Detection Mechanisms