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Software Fault Detection using Honey Bee Optimization

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  • D. Asir Antony Gnana Singh, A. Escalin Fernando, E. Jebamalar Leavline and PhD. 2017. Software Fault Detection using Honey Bee Optimization. International Journal of Applied Information Systems. 12, 1 (April 2017), 1-9. DOI=http://dx.doi.org/10.5120/ijais2017451565
  • @article{10.5120/ijais2017451565,
    		author = {D. Asir Antony Gnana Singh and A. Escalin Fernando and E. Jebamalar Leavline and PhD},
    		title = {Software Fault Detection using Honey Bee Optimization},
    		journal = {International Journal of Applied Information Systems},
    		issue_date = {April 2017},
    		volume = {12},
    		number = {1},
    		month = {Apr},
    		year = {2017},
    		issn = {2249-0868},
    		pages = {1-9},
    		numpages = {9},
    		url = {http://j34.ijais.org/archives/volume12/number1/968-2017451565},
    		doi = {10.5120/ijais2017451565},
    		publisher = {Foundation of Computer Science (FCS), NY, USA},
    		address = {New York, USA}
    	}
    
  • %0 Journal Article
    %1 2017451565
    %A D. Asir Antony Gnana Singh
    %A A. Escalin Fernando
    %A E. Jebamalar Leavline
    %A PhD
    %T Software Fault Detection using Honey Bee Optimization
    %J International Journal of Applied Information Systems
    %@ 2249-0868
    %V 12
    %N 1
    %P 1-9
    %D 2017
    %I Foundation of Computer Science (FCS), NY, USA
    

Abstract

The recent developments in the software technology assist humanities in various fields including engineering, technology, management, medical science, research, education, banking etc. Fault identification is a crucial one to the software testing professionals since a huge number of tests are carried out to identify the level of the defect. Therefore the machine learning algorithms are employed to develop software fault detection model in order to predict the fault in the software. The irrelevant and redundant test data reduces the accuracy of fault detection model. The accuracy of the fault detection model highly depends on the number of significant relevant test data. Therefore feature selection concept is applied to select the accurate features for developing the fault detection model. This paper proposes a method to select appropriate features with honey bee optimization technique for reducing the search space and to improve the accuracy in the software fault detection.

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Index Terms

Computer Science
Information Sciences

Keywords

D. Asir Antony Gnana Singh A. Escalin Fernando E. Jebamalar Leavline PhD Feature Selection Software Fault Detection Algorithm Honey Bee Optimization Data Mining Approaches Machine Learning Algorithm Improving Accuracy In Classification Algorithms.