NDLI: A similarity-aware approach to testing based fault localization

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A similarity-aware approach to testing based fault localization

Content Provider	ACM Digital Library
Author	Mei, Hong Pan, Ying Hao, Dan Zhang, Lu Zhao, Wei Sun, Jiasu
Abstract	Debugging is a time-consuming task in software development and maintenance. To accelerate this task, several approaches have been proposed to automate fault localization. In particular, testing based fault localization (TBFL), which utilizes the testing information to localize the faults, seem to be very promising. However, the similarity between test cases in the test suite has been ignored in the research on TBFL. In this paper, we investigate this similarity issue and propose a novel approach named similarity-aware fault localization (SAFL), which can calculate the suspicion probability of each statement with little impact by the similarity issue. To address and deal with the similarity between test cases, SAFL applies the theory of fuzzy sets to remove the uneven distribution of the test cases. We also performed an experimental study for two real-world programs at different size levels to evaluate SAFL together with another two approaches to TBFL. Experimental results show that SAFL is more effective than the other two approaches when the test suites contain injected redundancy, and SAFL can achieve a competitive result with normal test suites. SAFL can also be more effective than applying test suite reduction to current approaches to TBFL.
Starting Page	291
Ending Page	294
Page Count	4
File Format	PDF
ISBN	1581139934
DOI	10.1145/1101908.1101953
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fault localization Debugging Maintenance Fuzzy set
Content Type	Text
Resource Type	Article

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