Algorithms for clustering data
Algorithms for clustering data
Characterizing diagnoses and systems
Artificial Intelligence
The use of program profiling for software maintenance with applications to the year 2000 problem
ESEC '97/FSE-5 Proceedings of the 6th European SOFTWARE ENGINEERING conference held jointly with the 5th ACM SIGSOFT international symposium on Foundations of software engineering
An empirical investigation of program spectra
Proceedings of the 1998 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Visualization of test information to assist fault localization
Proceedings of the 24th International Conference on Software Engineering
Isolating cause-effect chains from computer programs
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
Basic Concepts and Taxonomy of Dependable and Secure Computing
IEEE Transactions on Dependable and Secure Computing
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
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
Statistical debugging: simultaneous identification of multiple bugs
ICML '06 Proceedings of the 23rd international conference on Machine learning
On the Accuracy of Spectrum-based Fault Localization
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
Automatic software fault localization using generic program invariants
Proceedings of the 2008 ACM symposium on Applied computing
A Crosstab-based Statistical Method for Effective Fault Localization
ICST '08 Proceedings of the 2008 International Conference on Software Testing, Verification, and Validation
The probabilistic program dependence graph and its application to fault diagnosis
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
Refining spectrum-based fault localization rankings
Proceedings of the 2009 ACM symposium on Applied Computing
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Abstract interpretation of programs for model-based debugging
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A practical evaluation of spectrum-based fault localization
Journal of Systems and Software
Evaluating Models for Model-Based Debugging
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
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
Zoltar: A Toolset for Automatic Fault Localization
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
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Background: Automated diagnosis of software defects can drastically increase debugging efficiency, improving reliability and time-to-market. Current, low-cost, automatic fault diagnosis techniques, such as spectrum-based fault localization (SFL), merely use information on whether a component is involved in a passed/failed run or not. However, these approaches ignore information on component execution frequency, which can improve the accuracy of the diagnostic process. Aim: In this paper, we study the impact of exploiting component execution frequency on the diagnostic quality. Method: We present a reasoning-based SFL approach, dubbed Zoltar-C, that exploits not only component involvement but also their frequency, using an approximate, Bayesian approach to compute the probabilities of the diagnostic candidates. Zoltar-C is evaluated and compared to other well-known, low-cost techniques (such as Tarantula) using a set of programs available from the Software Infrastructure Repository. Results: Results show that, although theoretically Zoltar-C can be of added value, exploiting component frequency does not improve diagnostic accuracy on average. Conclusions: The major reason for this unexpected result is the highly biased sample of passing and failing tests provided with the programs under analysis. In particular, the ratio between passing and failing runs, which has a major impact on the probability computations, does not correspond to the false negative (failure) rates associated with the actually injected faults.