Exploiting count spectra for Bayesian fault localization
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Diagnosing multiple intermittent failures using maximum likelihood estimation
Artificial Intelligence
The GZoltar project: a graphical debugger interface
TAIC PART'10 Proceedings of the 5th international academic and industrial conference on Testing - practice and research techniques
Simultaneous debugging of software faults
Journal of Systems and Software
An empirical study on the usage of testability information to fault localization in software
Proceedings of the 2011 ACM Symposium on Applied Computing
Prioritizing tests for fault localization through ambiguity group reduction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Towards a catalog of spreadsheet smells
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part IV
GZoltar: an eclipse plug-in for testing and debugging
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
On the empirical evaluation of fault localization techniques for spreadsheets
FASE'13 Proceedings of the 16th international conference on Fundamental Approaches to Software Engineering
An ontology toolkit for problem domain concept location in program comprehension
Proceedings of the 2013 International Conference on Software Engineering
A test-suite reduction approach to improving fault-localization effectiveness
Computer Languages, Systems and Structures
A dynamic code coverage approach to maximize fault localization efficiency
Journal of Systems and Software
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Locating software components which are responsible for observed failures is the most expensive, error-prone phase in the software development life cycle. Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important process for the development of dependable software. In this paper we present a toolset for automatic fault localization, dubbed Zoltar, which hosts a range of spectrum-based fault localization techniques featuring BARINEL, our latest algorithm. The toolset provides the infrastructure to automatically instrument the source code of software programs to produce runtime data, which is subsequently analyzed to return a ranked list of diagnosis candidates. Aimed at total automation (e.g., for runtime fault diagnosis), Zoltar has the capability of instrumenting the program under analysis with fault screeners as a run-time replacement for design-time test oracles.