## 4 Fault-Localization Method Using Memory-Updated Patterns
### Reducing the Fault Candidates  
We have described the detailed steps in the unit test phase for fault localization. In the integration test phase, the conversion of the collected memory data to the image file proceeds in the same way as the unit test. The image file contains the updated feature of the unit function and the featured frame. Figure 8: The basic form of the proposed neural network model. That is, the updated information of the featured addresses. Apply the proposed fault-localization method to these image files. The proposed method classifies the updated feature for the images of the app class with the traces of the function executed, except for the images of the infra class that are not directly related to the execution of the function. For each frame classified by the app class, the unit function and the featured frame class are identified, and to find a sequence in which a fault occurs that does not match the memory-updated pattern of normal operation. The updated feature classification uses the updated feature classifier created in the unit test phase. Each frame of the test in which the fault occurred is filtered by application frames corresponding to IDUR through the first stage classifier. Next, application frames classify the unit function and the featured frame in order, and output only the frames whose unit function index and featured frame index match the class. Figure 9 shows the flow through the updated feature classifier. In the figure, the image files of frames 214-221 are classified as the Infra/App class in the first stage. According to the flowchart, files 216, 218, and 219 are classified as the App class, and the matching of the unit function and the featured frame is confirmed. In the example, file 217 is classified as the unit function #4, the featured frame is classified as #5, but the featured frames matching the unit function #4 are #13-15. In this way, the unit function and the featured frame are matched, except for file 217. Finally, files 216, 218, and 219 are classified as application frames, the unit function index is #1, and the featured frame index is output as #1-3. The frames to which the class is assigned find the sequence corresponding to the updated feature, and compare it with the updated pattern of the normal operation, which presents a sequence other than the normal pattern as a fault occurrence point (i.e., unexpected sequences, nonsequential sequences, and disconnected sequences). The fault occurrence sequence and the previous sequence are presented as R1 and R2 ranks, respectively. In the rank, R1 must be checked first in the sequence in which the abnormal updated pattern occurs, and R2 may be the cause of the abnormal updated in the previous sequence. All sequences performed when a fault occurs may be involved in the fault, and are presented in R3. Addresses that cause the fault can be found by checking sequentially according to the rank. Figure 10 shows an example of the results presented by the fault-localization method proposed in this paper. In Figure 9, only three frames, 216, 218, and 219, are classified as applications. In (a), the sequence corresponding to the unit function index and the featured frame index of each frame, are sequentially from seq1 to seq3 of the front turn signal, and the next, seq4, does not appear. Therefore, R1 at the time of the fault occurrence is seq4, whose sequence is disconnected, R2 is seq3 that is the  
Figure 9: Flowchart of classification of updated features. previous sequence, and the other sequence is R3. The fault candidates to be presented along with the point of the fault occurrence are the featured addresses of each sequence. The fault candidates are R1 (#8619), R2 (#8612), and R3 (#8603, #8650, #10677, #10681) according to the normal pattern, as shown in (b). Algorithm 1 is described as to how to present a fault-occurrence point.