Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2076
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSaini, Ashish-
dc.contributor.authorKumar, Raj-
dc.contributor.authorKumar, Gaurav-
dc.contributor.authorKumar, Satendra-
dc.contributor.authorMittal, Mohit-
dc.date.accessioned2023-11-27T14:45:01Z-
dc.date.available2023-11-27T14:45:01Z-
dc.date.issued2022-10-19-
dc.identifier.citationVol.13, No.4; pp. 241-254en_US
dc.identifier.issn1755-9766-
dc.identifier.issn1755-9758-
dc.identifier.urihttps://dx.doi.org/10.1504/IJESMS.2022.126301-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2076-
dc.description.abstractTesting is a process that takes much time and effort in software companies. This becomes even more difficult and boring when it comes to testing a software product line (SPL). The SPL is a model in which multiple products from the same family are made simultaneously. Testing of all products is not possible. Hence a lot of testing methods have been given from time to time to test the product line, given by researchers based on contemporary conception. In the direction of testing product lines, this article has proposed a method, which used fuzzy C-means clustering with the Jaro-Winkler distance method. Variable features of the product form the basis for cluster development. The proposed method is compared with other distance methodologies. After comparison, it is concluded that the proposed method provides better results than other methods. This article has resorted to some product lines to compare with the proposed methods.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Engineering Systems Modelling and Simulationen_US
dc.subjectProduct lineen_US
dc.subjectSoftware product line testingen_US
dc.subjectFuzzy C-meansen_US
dc.subjectFCMen_US
dc.subjectFeature modelen_US
dc.subjectTestingen_US
dc.subjectSoftware industriesen_US
dc.titleSoftware Product Line Regression Testing Based on Fuzzy Clustering Approach Using Distance Methoden_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.