## 1 Introduction
The cause of the fault can be found by comparing the updated addresses in each unit test required for the normal operation of the integrated unit functions of the integration test. However, this method cannot specify when a fault occurs, and when the number of functions to be combined increases or becomes complicated, the major addresses of the executed functions become fault candidates, increasing the number of addresses to be checked. In addition, because this method does not know when the fault occurred, the interpretation of the results requires an understanding of the software's execution flow and a background knowledge of the address symbols and the stored values. Therefore, a method suitable for the HiL environment is needed that can identify the time of occurrence and cause of the fault without any special knowledge. In this paper, we propose a fault-localization method that uses updated patterns of memory in integrated testing. First, in the preparation step, the address, symbol, stored value and update frequency of the memory used in the normal unit test are analyzed to generate memory-updated information that is necessary for a normal operation of the function. The sequential pattern-mining algorithm [15] is applied to the generated memory-updated information to extract the memory-updated pattern for normal operation. The updated features of the collected memory are learned by a machine-learning model, comprising a convolutional neural network (CNN), using memory-updated information and an updated pattern. Next, in the integration test, the updated feature is classified in the memory data of each frame, and the fault-occurrence timing is found by pattern matching with the updated pattern of the normal operation. Figure 1 shows how the point of failure is found. In the integration test, a test script is constructed so that function A (four sequences) and B (five sequences) are successively called, such that the updated patterns of A and B appear in order. Therefore, if the integration test is executed normally,  
the memory-updated pattern should show the updated patterns of the two functions in order. However, in the memory-updated pattern of the failed integration test, A was found in order for all sequences, but seq4 and seq5 of B were repeated from frame #n, where B is called. The updated patterns of B have an abnormal pattern that is different from the normal operation at frame #n, and it can be seen that a fault occurs in frame #n. Therefore, if our debugging starts from addresses that should be updated to seq4 of B in frame #n, the cause can be found more quickly. As a result, the proposed method can find the occurrence point and the cause of the fault in the integration test by using the normal operation acquired in the unit test, without the source code or the debugging tool in the HiL environment. The proposed method is applied to the injected faults by the mutation method for evaluation in the HiL test of OSEK/VDX-based ECU/SW. The experimental results show the point of occurrence within an average of two frames for 80% faults. Only 3.28% of the memory addresses in the used memory are identified, and the cause of the fault can be found. This result shows that faults are found even if only 57% of the memory is checked by focusing on the fault candidates compared to previous studies on the same environment. The integration test can also indicate when a fault occurred without any special background. This paper is composed as follows. Section 2 describes related work in debugging in HiL environments and fault-localization methods using sequential pattern mining and CNN. Section 3 defines memory-updated features and pattern extraction for normal operation in memory. In Section 4, we propose a fault-localization method based on the learning method of the updated feature classifier and matching with the normal updated pattern. In Section 5, we configure the HiL environment and test the fault-localization method by injecting faults. Finally, Section 6 concludes with future work directions and conclusions.