## 5 Implementation and Experiment
### Finding the Updated Patterns and Building a Pattern Classifier  
The unit test of the SUT implanted with the fault is repeated to generate memory-updated information from the memory data in which the unit functions normally operate, and sequential pattern mining is applied to extract the normal memory-updated pattern. Table 2 shows the memory-updated patterns extracted from the SUT. The functions injected with faults in Table 1 consist of a combination of the unit functions shown in Table 2. For example, the driving mode of node 2 is a combination of the unit function gear and pedal. On the other hand, in the updated pattern of Table 2, the sequence may partially coincide, such as the front turn signal and the head lamp. This is because, even if the unit functions are different, a function similar to the case of allocating a signal on a bit-by-bit basis in the same memory in a communication process, may use a global variable for signal transmission. Both are part of the normal updated patterns. The memory-updated pattern of normal operation in Table 2 indicates the addresses to be updated when each unit function operates and the order to be updated. Therefore, it is possible to judge whether the function is normal by comparing the memory data when the function is operated and the pattern of Table 2.  
We generated the data to learn the updated feature using the updated patterns of the normal operation obtained. Based on the update of the featured addresses, the frame of the range in which the function operates in the unit test is converted into an image file and classified into a sequence. Infrastructure frames are sampled by the number of frames classified as applications in each unit test. Anaconda [30] and Keras [31] were used to generate and learn the update feature classifier. \begin{table}
\begin{tabular}{c c c c} \hline
**Node** & **Unit Function** & **Number of Sequences** & **Featured Addresses** \\ \hline \multirow{4}{*}{1} & Front Turn Signal & 5 & (8650,10677,10681), (8603), (8611,8612,8613), (8619), \\  & Head Lamp & 5 & (8624,8625,8626) \\  & Steering & 4 & (8650,10677,10681), (8603), (8605,8608,8609), \\  & Sensor & 3 & (8615,8610,8617,8618), (8620,8622,8623) \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{AEBS} & \multirow{4}{*}{4} & (8651,10677,10681), (8639), (8640), (8641) \\  & & & (8628,8633,8935,10604), (8637), (8604) \\ \cline{1-1}  & & & (8696), (8700,8704), (8668,8669,8704), (8661), (8628), \\ \cline{1-1}  & AEBS & 9 & (8701,8703,8704), (8671,8672,8678,8683,8688,8704), \\ \cline{1-1}  & & & (8664), (8624,8627) \\ \cline{1-1}  & & 4 & (8700,8704), (8668,8669,8704), (8661), (8628) \\ \cline{1-1}  & & & (8701,8703,8704), (8671,8672,8673,8683,8688), (8664), \\ \cline{1-1}  & & & (8624,8627) \\ \hline \multirow{4}{*}{3} & Rear Turn Signal & 5 & (8646), (8606), (8607,8608,8609), (8621), \\ \cline{1-1}  & & & (8624,8625,8626) \\ \cline{1-1}  & & & (8646,8648), (8613,8614), (8610,8611,8619,8620), \\ \cline{1-1}  & & & (8622,8623), (8627,8628) \\ \cline{1-1}  & Rear Alert & 4 & (8656), (8618), (8646), (8602,8613) \\ \hline \end{tabular}
\end{table} 
Table 2: List of memory-updated patterns of the system under test (SUT). The data set for learning consisted of about 4000 image data. For the featured frame of 48 sequences of memory-updated patterns in Table 2, image data of 40 app class on the average were prepared, and data of infra class of the similar amount were also prepared. These image data contain labels of three classes that are necessary for the learning of the three classifiers. Use this dataset to learn three classifiers for each node, a total of nine classifiers. The dataset was constructed considering the ratio of the featured frame. Therefore, when learning the unit function classifier, the data ratio between classes should be adjusted evenly. Among the data sets, the ratio of training to validation is 4:1. Table 3 shows the accuracy, precision, recall, and F1-score for the validation data as a result of learning. In this case, the F1-score is the harmonic mean of precision and recall.