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Many software applications must provide services to hundreds or thousands of users concurrently. These applications must be load tested to ensure that they can function correctly under high load. Problems in load testing are due to problems in the load environment, the load generators, and the application under test. It is important to identify and address these problems to ensure that load testing results are correct and these problems are resolved. It is difficult to detect problems in a load test due to the large amount of data which must be examined. Current industrial practice mainly involves time-consuming manual checks which, for example, grep the logs of the application for error messages. In this paper, we present an approach which mines the execution logs of an application to uncover the dominant behavior (i.e., execution sequences) for the application and flags anomalies (i.e., deviations) from the dominant behavior. Using a case study of two open source and two large enterprise software applications, we show that our approach can automatically identify problems in a load test. Our approach flags < 0.01% of the log lines for closer analysis by domain experts. The flagged lines indicate load testing problems with a relatively small number of false alarms. Our approach scales well for large applications and is currently used daily in practice.
Date of Conference: Sept. 28 2008-Oct. 4 2008