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http://hdl.handle.net/2080/3119
Title: | Effective Software Fault Localization using an Back Propagation Neural Network |
Authors: | Maru, Abha Dutta, Arpita Kumar, Vinod Mohapatra, Durga Prasad |
Keywords: | Back-Propagation neural network Program debugging Fault localization Suspiciousness of code Failed test Successful test |
Issue Date: | Dec-2018 |
Citation: | International Conference on Computational Intelligence in Data Mining (ICCIDM 2018), Burla, Odisha, India, 15-16 December, 2018 |
Abstract: | Effective fault localization is an essential requirement of software development process. Backpropagation neural network can be used for localizing the faults effectively and efficiently. Existing NN based fault localization techniques take statement invocation information in binary terms to train the network. In this paper, we have proposed an efficient approach for fault localization using back propagation neural network and we have used the actual number of times the statement is executed to train the network. We have investigated our approach on Siemens suite. Results show that on an average there is 35% increase in the effectiveness over existing BPNN. |
Description: | Copyright of this document belongs to proceedings publisher. |
URI: | http://hdl.handle.net/2080/3119 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2018_ICCIDM_DPMohapatra_EffectiveSoftware.pdf | Conference paper | 376.88 kB | Adobe PDF | View/Open |
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