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
Visualization of test information to assist fault localization
Proceedings of the 24th International Conference on Software Engineering
ECOOP '01 Proceedings of the 15th European Conference on Object-Oriented Programming
Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data 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
Mining metrics to predict component failures
Proceedings of the 28th international conference on 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)
Extraction of bug localization benchmarks from history
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
Using Static Analysis to Find Bugs
IEEE Software
Identifying bug signatures using discriminative graph mining
Proceedings of the eighteenth international symposium on Software testing and analysis
Why Programs Fail, Second Edition: A Guide to Systematic Debugging
Why Programs Fail, Second Edition: A Guide to Systematic Debugging
Fault localization based on information flow coverage
Software Testing, Verification & Reliability
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
IEEE Transactions on Software Engineering
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Defect localisation is essential in software engineering and is an important task in domain-specific data mining. Existing techniques building on call-graph mining can localise different kinds of defects. However, these techniques focus on defects that affect the controlflow and are agnostic regarding the dataflow. In this paper, we introduce dataflowenabled call graphs that incorporate abstractions of the dataflow. Building on these graphs, we present an approach for defect localisation. The creation of the graphs and the defect localisation are essentially data mining problems, making use of discretisation, frequent subgraph mining and feature selection. We demonstrate the defect-localisation qualities of our approach with a study on defects introduced into Weka. As a result, defect localisation now works much better, and a developer has to investigate on average only 1.5 out of 30 methods to fix a defect.