Critical slicing for software fault localization
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
Simplifying and Isolating Failure-Inducing Input
IEEE Transactions on Software Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Computing Frequent Graph Patterns from Semistructured Data
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
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
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
Pruning dynamic slices with confidence
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Mining specifications of malicious behavior
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
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
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
GAIA: graph classification using evolutionary computation
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Causal inference for statistical fault localization
Proceedings of the 19th international symposium on Software testing and analysis
Diagnosing memory leaks using graph mining on heap dumps
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Scalable graph analyzing approach for software fault-localization
Proceedings of the 6th International Workshop on Automation of Software Test
Are automated debugging techniques actually helping programmers?
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Dual active feature and sample selection for graph classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Software fault localization via mining execution graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Statistical debugging using a hierarchical model of correlated predicates
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Inferred dependence coverage to support fault contextualization
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Querying subgraph sets with g-tries
DBSocial '12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks
Practical isolation of failure-inducing changes for debugging regression faults
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Griffin: grouping suspicious memory-access patterns to improve understanding of concurrency bugs
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Mining succinct predicated bug signatures
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Graph classification with imbalanced class distributions and noise
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Although such methods could be very accurate, it is usually very hard for developers to understand the context of the bug, given each bug location in isolation. In this study, we propose to model software executions with graphs at two levels of granularity: methods and basic blocks. An individual node represents a method or basic block and an edge represents a method call, method return or transition (at the method or basic block granularity). Given a set of graphs of correct and faulty executions, we propose to extract the most discriminative subgraphs which contrast the program flow of correct and faulty executions. The extracted subgraphs not only pinpoint the bug, but also provide an informative context for understanding and fixing the bug. Different from traditional graph mining which mines a very large set of frequent subgraphs, we formulate subgraph mining as an optimization problem and directly generate the most discriminative subgraph with a recently proposed graph mining algorithm LEAP. We further extend it to generate a ranked list of top-k discriminative subgraphs representing distinct locations which may contain bugs. Experimental results and case studies show that our proposed method is both effective and efficient to mine discriminative subgraphs for bug localization and context identification.