Testability of Software Components
IEEE Transactions on Software Engineering
PIE: A Dynamic Failure-Based Technique
IEEE Transactions on Software Engineering
Semantic metrics for software testability
Journal of Systems and Software - Special issue on the Oregon Metric Workshop
A semantic model of program faults
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
An experimental determination of sufficient mutant operators
ACM Transactions on Software Engineering and Methodology (TOSEM)
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
Testability, fault size and the domain-to-range ratio: An eternal triangle
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Prioritizing Test Cases For Regression Testing
IEEE Transactions on Software Engineering
Visualization of test information to assist fault localization
Proceedings of the 24th International Conference on Software Engineering
Software Testability: The New Verification
IEEE Software
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
A new approach for software testability analysis
Proceedings of the 28th international conference on Software engineering
An empirical study into class testability
Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
On the Accuracy of Spectrum-based Fault Localization
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
A Crosstab-based Statistical Method for Effective Fault Localization
ICST '08 Proceedings of the 2008 International Conference on Software Testing, Verification, and Validation
Cooperative debugging with five hundred million test cases
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
An observation-based model for fault localization
WODA '08 Proceedings of the 2008 international workshop on dynamic analysis: held in conjunction with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2008)
A new bayesian approach to multiple intermittent fault diagnosis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Diagnosing multiple persistent and intermittent faults
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Spectrum-Based Multiple Fault Localization
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Adaptive Random Test Case Prioritization
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Zoltar: A Toolset for Automatic Fault Localization
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Prioritizing tests for fault localization through ambiguity group reduction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
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When failures occur during software testing, automated software fault localization helps to diagnose their root causes and identify the defective statements of a program to support debugging. Diagnosis is carried out by selecting test cases in such way that their pass or fail information will narrow down the set of fault candidates, and, eventually, pinpoint the root cause. An essential in gredient of effective and efficient fault localization is knowledge about the false negative rate of tests, which is related to the rate at which defective statements of a program will exhibit failures. In current fault localization processes, false negative rates are either ignored completely, or merely estimated a posteriori as part of the diagnosis. In this paper, we study the reduction in diagnosis effort when false negative rates are known a priori. We deduce this information from testability, following the propagation-infection-execution (PIE) approach. Experiments with real programs suggest significant improvement in the diagnosis process, both in the single and the multiple-fault cases. When compared to the next-best technique, PIE-based false negative rate information yields a fault localization effort reduction of up to 80% for systems with only one fault, and up to 60% for systems with multiple faults.