Information Processing Letters
C4.5: programs for machine learning
C4.5: programs for machine learning
A methodology for controlling the size of a test suite
ACM Transactions on Software Engineering and Methodology (TOSEM)
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Technical Note: Visually Encoding Program Test Information to Find Faults in Software
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery from Transportation Network Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Predicting defect densities in source code files with decision tree learners
Proceedings of the 2006 international workshop on Mining software repositories
Predicting component failures at design time
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Discriminative pattern mining in software fault detection
Proceedings of the 3rd international workshop on Software quality assurance
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Localizing defects in multithreaded programs by mining dynamic call graphs
TAIC PART'10 Proceedings of the 5th international academic and industrial conference on Testing - practice and research techniques
Software-defect localisation by mining dataflow-enabled call graphs
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
On graph query optimization in large networks
Proceedings of the VLDB Endowment
Scalable graph analyzing approach for software fault-localization
Proceedings of the 6th International Workshop on Automation of Software Test
LGM: mining frequent subgraphs from linear graphs
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Software fault localization via mining execution graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
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An important problem in software engineering is the automated discovery of noncrashing occasional bugs. In this work we address this problem and show that mining of weighted call graphs of program executions is a promising technique. We mine weighted graphs with a combination of structural and numerical techniques. More specifically, we propose a novel reduction technique for call graphs which introduces edge weights. Then we present an analysis technique for such weighted call graphs based on graph mining and on traditional feature selection schemes. The technique generalises previous graph mining approaches as it allows for an analysis of weights. Our evaluation shows that our approach finds bugs which previous approaches cannot detect so far. Our technique also doubles the precision of finding bugs which existing techniques can already localise in principle.