Deep learning has revolutionized various real-world applications, but the quality of Deep Neural Networks (DNNs) remains a concern. DNNs are complex and have millions of parameters, making it difficult to determine their contributions to fulfilling a task. Moreover, the behavior of a DNN is highly influenced by the data used during training, making it challenging to collect enough data to exercise all potential DNN behavior under all possible scenarios. This paper proposes a novel NP-SBFL method that adapts spectrum-based fault localization (SBFL) to locate faulty neural pathways. Our method identifies critical neurons using the layer-wise relevance propagation (LRP) technique and determines which critical neurons are faulty. We propose a multi-stage gradient ascent (MGA), an extension of gradient ascent, to effectively activate a sequence of neurons one at a time while maintaining the activation of previous neurons. We evaluated the effectiveness of our method on two commonly used datasets, MNIST and CIFAR-10, two baselines DeepFault and NP-SBFL-GA, and three suspicious neuron measures, Tarantula, Ochiai, and Barinel. The empirical results showed that NP-SBFL-MGA is statistically more effective than the baselines at identifying suspicious paths and synthesizing adversarial inputs. Particularly, Tarantula on NP-SBFL-MGA had the highest fault detection rate at 96.75%, surpassing DeepFault on Ochiai (89.90%) and NP-SBFL-GA on Ochiai (60.61%). Our approach also yielded comparable results to the baselines in synthesizing naturalness inputs, and we found a positive correlation between the coverage of critical paths and the number of failed tests in DNN fault localization.

这篇论文提出了一种新颖的NP-SBFL方法，它通过使用层次相关传播（LRP）技术来确定关键神经元并确定哪些关键神经元是有问题的。该方法通过多阶段梯度上升（MGA）有效激活一系列神经元，并在维持以前神经元的激活的同时确定故障神经元。实证结果表明，NP-SBFL-MGA在发现可疑路径和合成对抗输入方面比基准方法具有更高的效果。尤其是，在NP-SBFL-MGA上，Tarantula方法的故障检测率为96.75％，超过Ochiai方法上的DeepFault（89.90％）和NP-SBFL-GA（60.61％）。我们的方法在合成自然输入方面也产生了可比较的结果，并发现关键路径的覆盖率与DNN故障定位中的失败测试数之间存在正相关关系。

NP-SBFL：连接基于频谱的故障定位与错误的神经途径诊断间的鸿沟