Automatic Abnormal Log Detection by Analyzing Log History for Providing Debugging Insight

As the size of software becomes larger and more complex, finding the cause of defects becomes increasingly difficult. Moreover, it is hard to reproduce defects when many components such as processes in platform environment or devices in IoT environment are involved. In this case, analyzing logs are the only way to get debugging insights, but manual log analysis is highly labor intensive work. In this paper, we propose a new log analysis system called historian which runs based on history of test logs. Our system first computes importance and noise scores of each log line by using statistical text mining techniques, and then highlights abnormal log lines based on computed scores for providing debugging insights. We applied historian to Tizen Native API test logs, and our system highlighted only about 4% log lines in average. We also provided highlighted failed logs to Tizen developers and the developers said that failure related log lines were highlighted well. These experimental results show that our system effectively highlights abnormal log lines and provides debugging insights to developers.