CNN-based fault localization method using memory-updated patterns for integration test in an HiL environment - Choi, Ki Yong; Lee, Jung Won (Applied Sciences (Switzerland), 2019)
Automotive electronic components are tested via hardware-in-the-loop (HiL) testing at the unit and integration test stages, according to ISO 26262. It is difficult to obtain debugging information from the HiL test because the simulator runs a black-box test automatically, depending on the scenario in the test script. At this time, debugging information can be obtained in HiL tests, using memory-updated information, without the source code or the debugging tool. However, this method does not know when the fault occurred, and it is difficult to select the starting point of debugging if the execution flow of the software is not known. In this paper, we propose a fault-localization method using a pattern in which each memory address is updated in the HiL test. Via a sequential pattern-mining algorithm in the memory-updated information of the transferred unit tests, memory-updated patterns are extracted, and the system learns using a convolutional neural network. Applying the learned pattern in the memory-updated information of the integration test can determine the fault point from the normal pattern. The point of departure from the normal pattern is highlighted as a fault-occurrence time, and updated addresses are presented as fault candidates. We applied the proposed method to an HiL test of an OSEK/VDX-based electronic control unit. Through fault-injection testing, we could find the cause of faults by checking the average memory address of 3.28%, and we could present the point of fault occurrence with an average accuracy of 80%.
Keywords: Automotive software; Convolutional neural network (CNN); Fault localization; Hardware-in-the-loop (HiL); Sequential pattern mining