Context-sensitive delta inference for identifying workload-dependent performance bottlenecks
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Pathways to technology transfer and adoption: achievements and challenges (mini-tutorial)
Proceedings of the 2013 International Conference on Software Engineering
Software analytics: achievements and challenges
Proceedings of the 2013 International Conference on Software Engineering
Report on the international symposium on high confidence software (ISHCS 2011/2012)
ACM SIGSOFT Software Engineering Notes
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Monitoring and diagnosing performance issues of an online service system are critical to assure satisfactory performance of the system. Given a detected performance issue and collected system metrics for an online service system, engineers usually need to make great efforts to conduct diagnosis by first identifying performance issue beacons, which are metrics that pinpoint to the root causes. In order to reduce the manual efforts, in this paper, we propose a new approach to effectively detecting performance issue beacons to help with performance issue diagnosis. Our approach includes techniques for mining system metric data to address limitations when applying previous classification-based approaches. Our evaluations on both a controlled environment and a real production environment show that our approach can more effectively identify performance issue beacons from system metric data than previous approaches.