Dustminer: troubleshooting interactive complexity bugs in sensor networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Software Fault Localization Using N-gram Analysis
WASA '08 Proceedings of the Third International Conference on Wireless Algorithms, Systems, and Applications
Finding Symbolic Bug Patterns in Sensor Networks
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Efficient mining of recurrent rules from a sequence database
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Practical experiences with chronics discovery in large telecommunications systems
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
Practical experiences with chronics discovery in large telecommunications systems
ACM SIGOPS Operating Systems Review
Effective software fault localization by statistically testing the program behavior model
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
Improving failure-inducing changes identification using coverage analysis
Proceedings of the 34th International Conference on Software Engineering
Practical isolation of failure-inducing changes for debugging regression faults
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
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Manual debugging is expensive. And the high cost has motivated extensive research on automated fault lo- calization in both software engineering and data mining communities. Fault localization aims at automatically locating likely fault locations, and hence assists manual debugging. A number of fault localization algorithms have been developed in recent years, which prove effec- tive when multiple failing and passing cases are avail- able. However, we notice what is more commonly en- countered in practice is the two-sample debugging prob- lem, where only one failing and one passing cases are available. This problem has been either overlooked or insufficiently tackled in previous studies. In this paper, we develop a new fault localization al- gorithm, named BayesDebug, which simulates some manual debugging principles through a Bayesian ap- proach. Different from existing approaches that base fault analysis on multiple passing and failing cases, BayesDebug only requires one passing and one failing cases. We reason about why BayesDebug fits the two- sample debugging problem and why other approaches do not. Finally, an experiment with a real-world program grep-2.2 is conducted, which exemplifies the effective- ness of BayesDebug.