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Oct 2024 - Volume 15, Issue 5
Deadline: 15 Sep 2024
Publication: 20 Oct 2024
Dec 2024 - Volume 15, Issue 6
Deadline: 15 Nov 2024
Publication: 20 Dec 2024
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ABSTRACT
Title |
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An Empirical Study On Fault Localization And Effective Test Case Selection By Neural Network |
Authors |
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A. Pravin, Dr. S. Srinivasan |
Keywords |
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RBF neural network, Fault localization, ranking. |
Issue Date |
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Dec 2012-Jan 2013 |
Abstract |
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A Radial basis function (RBF) neural network based fault localization technique is proposed in this paper to assist programmers in locating bugs effectively. Here we employ a three-layered feed forward artificial neural network with a radial basis function for its hidden unit activation and for linear function with its output layer activation. Here the neural network is trained to have a good relationship between the statement coverage information of a test case and its corresponding execution result to get a success or failure. The trained network is then given as an input to a set of virtual test cases, each covering a single statement, and the output of the network, for each virtual test case, is considered to be the suspiciousness of the corresponding covered statement. A statement with a higher suspiciousness has a higher likelihood of contain a bug, and thus, statement can be ranked in descending order of their suspiciousness. The Ranking can then be examined one by one, starting from the top, until a bug is located. Six case studies on different programs were conduced, with each faulty version contain a distinct bug, and the result clearly show that our proposed technique is much more effective than Tarantula, another popular fault localization technique. |
Page(s) |
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812-817 |
ISSN |
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0976-5166 |
Source |
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Vol. 3, No.6 |
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