一种变异真值表故障模型的神经网络测试生成算法

Testing generation algorithm based on neural networks for mutated truth table fault model

针对传统测试生成算法计算复杂度高的问题,提出一种针对逻辑门功能异常的故障模型,并给出了基于遗传优化的神经网络测试生成算法。首先,与传统算法以固定值故障为目标不同,构建更全面的变异真值表故障模型,在考虑各输入条件下故障的不同权重的同时,按故障模型自动生成故障字典;然后,测试生成算法利用逻辑门的二值神经网络能量函数,构成数字电路的约束网络;最终,调用故障字典向约束网络注入故障,通过遗传算法求解出被测电路的测试集。仿真实验结果显示,所提的故障模型更加全面,且测试生成算法具有正确性和有效性。

In order to solve the problem of the high complexity of the conditional test generation methods, a new fault model based on functional fails and neural network test generation algorithmare proposed.At first, different from conditional methods based on stuck-at fault model, a mutated truth table fault model is built considering the weights of faults under different inputs. The fault dictionary is automatically formed by fault model. Secondly, the constraint network is constructed with energy function of two-valued neural network. Lastly, faults are injected into the constraint network referring to the fault model, and the testing sets are get by genetic Algorithm. The experimental results show that the fault model and the test generation method are effective.