Brief Announcement: Automatic Log Enhancement for Fault Diagnosis

Abstract

When systems fail, logs are frequently the only evidence available for underlying fault diagnosis. Consequently, the quality of logs-how well system faults can be reflected by these log messages, is of significant importance. To improve the quality of logs, we propose a novel log enhancement approach which automatically identifies logging points that reflect anomalous behavior during system fault time. We further evaluate our approach with three popular open source projects. Results show that it can significantly improve over 50% accuracy of automatic fault diagnosis on average.