Mining Historical Test Logs to Predict Bugs and Localize Faults in the Test Logs

Abstract

Software testing is an integral part of modern software development. However, test runs can produce thousands of lines of logged output that make it difficult to find the cause of a fault in the logs. This problem is exacerbated by environmental failures that distract from product faults. In this paper we present techniques with the goal of capturing the maximum number of product faults, while flagging the minimum number of log lines for inspection. We observe that the location of a fault in a log should be contained in the lines of a failing test log. In contrast, a passing test log should not contain the lines related to a failure. Lines that occur in both a passing and failing log introduce noise when attempting to find the fault in a failing log. We introduce an approach where we remove the lines that occur in the passing log from the failing log. After removing these lines, we use information retrieval techniques to flag the most probable lines for investigation. We modify TF-IDF to identify the most relevant log lines related to past product failures. We then vectorize the logs and develop an exclusive version of KNN to identify which logs are likely to lead to product faults and which lines are the most probable indication of the failure. Our best approach, LogFaultFlagger finds 89% of the total faults and flags less than 1% of the total failed log lines for inspection. LogFaultFlagger drastically outperforms the previous work CAM. We implemented LogFaultFlagger as a tool at Ericsson where it presents fault prediction summaries to base station testers.