C4.5: programs for machine learning
C4.5: programs for machine learning
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
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
Locating faults through automated predicate switching
Proceedings of the 28th international conference on Software engineering
Statistical debugging using compound boolean predicates
Proceedings of the 2007 international symposium on Software testing and analysis
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
HOLMES: Effective statistical debugging via efficient path profiling
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Classification of software behaviors for failure detection: a discriminative pattern mining approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying bug signatures using discriminative graph mining
Proceedings of the eighteenth international symposium on Software testing and analysis
Minimum description length principle: generators are preferable to closed patterns
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Causal inference for statistical fault localization
Proceedings of the 19th international symposium on Software testing and analysis
Comprehensive evaluation of association measures for fault localization
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Are automated debugging techniques actually helping programmers?
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Reducing confounding bias in predicate-level statistical debugging metrics
Proceedings of the 34th International Conference on Software Engineering
Mining explicit rules for software process evaluation
Proceedings of the 2013 International Conference on Software and System Process
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A bug signature is a set of program elements highlighting the cause or effect of a bug, and provides contextual information for debugging. In order to mine a signature for a buggy program, two sets of execution profiles of the program, one capturing the correct execution and the other capturing the faulty, are examined to identify the program elements contrasting faulty from correct. Signatures solely consisting of control flow transitions have been investigated via discriminative sequence and graph mining algorithms. These signatures might be handicapped in cases where the effect of a bug is not manifested by any deviation in control flow transitions. In this paper, we introduce the notion of predicated bug signature\/ that aims to enhance the predictive power of bug signatures by utilizing both data predicates and control-flow information. We introduce a novel ``discriminative itemset generator'' mining technique to generate succinct\/ signatures which do not contain redundant or irrelevant program elements. Our case studies demonstrate that predicated signatures can hint at more scenarios of bugs where traditional control-flow signatures fail.