Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Kernel independent component analysis
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Pin: building customized program analysis tools with dynamic instrumentation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Statistical debugging: simultaneous identification of multiple bugs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Problem diagnosis in large-scale computing environments
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Statistical Debugging: A Hypothesis Testing-Based Approach
IEEE Transactions on Software Engineering
Real-time data driven deformation using kernel canonical correlation analysis
ACM SIGGRAPH 2008 papers
Lessons learned at 208K: towards debugging millions of cores
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Statistical Debugging Using Latent Topic Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
HOLMES: Effective statistical debugging via efficient path profiling
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Vrisha: using scaling properties of parallel programs for bug detection and localization
Proceedings of the 20th international symposium on High performance distributed computing
WuKong: automatically detecting and localizing bugs that manifest at large system scales
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
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A key challenge in developing large scale applications (both in system size and in input size) is finding bugs that are latent at the small scales of testing, only manifesting when a program is deployed at large scales. Traditional statistical techniques fail because no error-free run is available at deployment scales for training purposes. Prior work used scaling models to detect anomalous behavior at large scales without being trained on correct behavior at that scale. However, that work cannot localize bugs automatically. In this paper, we extend that work with automatic diagnosis technique, based on feature reconstruction, and validate our design through case studies with two real bugs from an MPI library and a DHT-based file sharing application.