Tracking down software bugs using automatic anomaly detection
Proceedings of the 24th International Conference on Software Engineering
The Future of Systems Research
Computer
Proactive Detection of Software Aging Mechanisms in Performance Critical Computers
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
Multi-resolution Abnormal Trace Detection Using Varied-length N-grams and Automata
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Rx: treating bugs as allergies---a safe method to survive software failures
Proceedings of the twentieth ACM symposium on Operating systems principles
HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
Detecting application-level failures in component-based Internet services
IEEE Transactions on Neural Networks
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Large software systems are extremely complex and based on code that is constantly changing with bug fixes and new features. As a result, these systems will likely never be free of bugs. The bugs typically don't expose themselves until they are triggered by a new workload, and when triggered, they are rarely immediately fatal, but result in a system that continues to run with corrupt internal state, deteriorating over time to the point where it becomes inoperable. Having a method to identify corrupt state early would allow the initiation of defensive actions such as flushing page caches or redirecting external requests to another service in the cluster. In this paper, we propose a statistical method of detecting problems in software at run-time based on analyzing function return values. The methodology, at this time, requires the availability of source code, but does not require understanding the source code. Our experimental results indicate that our method can be effective in identifying problems early on, potentially allowing for defensive measures. The overhead is negligible at less than 1%.