Neural Network Methodology for Embedded System Testing

This paper describes testing framework that is capable of testing heterogeneous embedded systems. Here, we present a new concept of using an artificial neural network as an automated oracle for a tested software system. A neural network is trained by the back propagation algorithm on a set of test cases applied to the original version of the system. The network training is based on the “black-box” approach, since only inputs and outputs of the system are presented to the algorithm. A set of different experiments in the domain of testing of embedded system is presented. The trained network can be used as an artificial oracle for evaluating the correctness of the output produced by new and possibly faulty versions of the software. We present experimental results of using a two-layer neural network to detect faults within mutated code of a small credit approval application. This approach is capable to testing of host based and target based embedded systems.