Predicting Software Reliability using Computational Intelligence Techniques: A Review

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3145

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DC Field	Value	Language
dc.contributor.author	Kumar, Kulamala Vinod	-
dc.contributor.author	Teja, A. Sarath Chandra	-
dc.contributor.author	Maru, Abha	-
dc.contributor.author	Singla, Yogesh	-
dc.contributor.author	Mohapatra, Durga Prasad	-
dc.date.accessioned	2019-01-01T13:04:31Z	-
dc.date.available	2019-01-01T13:04:31Z	-
dc.date.issued	2018-12	-
dc.identifier.citation	17th International Conference on Industrial Technology (ICIT 2018), Bhubaneswar , India, 20-22 December 2018	en_US
dc.identifier.uri	http://hdl.handle.net/2080/3145	-
dc.description	Copyright of this document belongs to proceedings publisher.	en_US
dc.description.abstract	Software measurement is yet in an infant stage. There is hardly any efficient quantitative method to represent software reliability. The existing methods are not generic and have many limitations. Various techniques could be used to enhance software reliability. However, one has to not only balance time but also cater to budget constraints. Computational Intelligence (CI) techniques that have been explored for software reliability prediction have shown remarkable results. In this paper, the applications of CI techniques for software reliability prediction are surveyed and an evaluation based on some selected performance criteria is presented.	en_US
dc.subject	Software reliability	en_US
dc.subject	Assessment	en_US
dc.subject	Fault prediction	en_US
dc.subject	Computational intelligence techniques	en_US
dc.title	Predicting Software Reliability using Computational Intelligence Techniques: A Review	en_US
dc.type	Article	en_US
Appears in Collections:	Conference Papers

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