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An empirical study of the effects of test-suite reduction on fault localization
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International Conference on Software Engineering archive
Proceedings of the 30th international conference on Software engineering table of contents
Leipzig, Germany
SESSION: Empirical software engineering table of contents
Pages 201-210  
Year of Publication: 2008
ISBN:978-1-60558-079-1
Authors
Yanbing Yu  Georgia Institute of Technology, Atlanta, GA, USA
James A. Jones  Georgia Institute of Technology, Atlanta, GA, USA
Mary Jean Harrold  Georgia Institute of Technology, Atlanta, GA, USA
Sponsors
ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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ABSTRACT

Fault-localization techniques that utilize information about all test cases in a test suite have been presented. These techniques use various approaches to identify the likely faulty part(s) of a program, based on information about the execution of the program with the test suite. Researchers have begun to investigate the impact that the composition of the test suite has on the effectiveness of these fault-localization techniques. In this paper, we present the first experiment on one aspect of test-suite composition--test-suite reduction. Our experiment studies the impact of the test-suite reduction on the effectiveness of fault-localization techniques. In our experiment, we apply 10 test-suite reduction strategies to test suites for eight subject programs. We then measure the differences between the effectiveness of four existing fault-localization techniques on the unreduced and reduced test suites. We also measure the reduction in test-suite size of the 10 test-suite reduction strategies. Our experiment shows that fault-localization effectiveness varies depending on the test-suite reduction strategy used, and it demonstrates the trade-offs between test-suite reduction and fault-localization effectiveness.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Yanbing Yu: colleagues
James A. Jones: colleagues
Mary Jean Harrold: colleagues