Mathematical statistics (4th ed.)
Mathematical statistics (4th ed.)
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 Software Engineering
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Discriminative pattern mining in software fault detection
Proceedings of the 3rd international workshop on Software quality assurance
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
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining Edge-Weighted Call Graphs to Localise Software Bugs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
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
Identifying bug signatures using discriminative graph mining
Proceedings of the eighteenth international symposium on Software testing and analysis
Rapid: Identifying Bug Signatures to Support Debugging Activities
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Argus: online statistical bug detection
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
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In this paper, a new approach for analyzing program behavioral graphs to detect fault relevant paths is presented. The existing graph mining approaches for bug localization merely detect discriminative sub-graphs between failing and passing runs. However, they are not applicable when the context of a failure is not appeared in a discriminative pattern. In our proposed method, the suspicious transitions are identified by contrasting nearest neighbor failing and passing dynamic behavioral graphs. For finding similar failing and passing graphs, we first convert the graphs into adequate vectors. Then, a combination of Jacard-Cosine similarity measures is applied to identify the nearest graphs. The new scoring formula takes advantage of null hypothesis testing for ranking weighted transitions. The main advantage of the proposed technique is its scalability which makes it work on large and complex programs with huge number of predicates. Another main capability of our approach is providing the faulty paths constructed from fault suspicious transitions. Considering the weighted execution graphs in the analysis enables us to find those types of bugs which reveal themselves in specific number of transitions between two particular predicates. The experimental results on Siemens test suite and Space program manifest the effectiveness of the proposed method on weighted execution graphs for locating bugs in comparison with other methods.