Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Why Programs Fail: A Guide to Systematic Debugging
Why Programs Fail: A Guide to Systematic Debugging
SOBER: statistical model-based bug localization
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Cooperative bug isolation
Statistical debugging: simultaneous identification of multiple bugs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Statistical debugging using compound boolean predicates
Proceedings of the 2007 international symposium on Software testing and analysis
Context-aware statistical debugging: from bug predictors to faulty control flow paths
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Finding Causes of Software Failure Using Ridge Regression and Association Rule Generation Methods
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Identifying bug signatures using discriminative graph mining
Proceedings of the eighteenth international symposium on Software testing and analysis
Argus: online statistical bug detection
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
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The aim of statistical debugging is to identify faulty predicates that have strong effect on program failure. In this paper predicates are fitted into a linear regression model to consider the vertical effect of predicates on each other and on program termination status. Prior approaches have merely considered predicates in isolation. The proposed approach in this paper is a twostep procedure which includes hierarchical clustering and the Lasso regression method. Hierarchical clustering builds a tree structure of correlated predicates. The Lasso method is applied on the clusters in some specified levels of the tree. This makes the method scalable in terms of the size of a program. Unlike other statistical methods which do not provide any context of the failure, the predicates contained in the group that is provided by this method can be used as the bug signature. The method has been evaluated on two well-known test suites, Space and Siemens. The experimental results reveal the accuracy and precision of the approach comparing with similar techniques.