Please use this identifier to cite or link to this item: https://elib.vku.udn.vn/handle/123456789/3955
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDo, Van Nho-
dc.contributor.authorNguyen, Quang Vu-
dc.contributor.authorNguyen, Thanh Binh-
dc.date.accessioned2024-07-29T02:48:28Z-
dc.date.available2024-07-29T02:48:28Z-
dc.date.issued2023-08-
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/24751839.2023.2252186-
dc.identifier.urihttps://elib.vku.udn.vn/handle/123456789/3955-
dc.descriptionJournal of Information and Telecommunication; Vol.8, Issue 1; pp: 57-70vi_VN
dc.description.abstractIn software testing, the quality of the test suite plays a very important role for not only the effectiveness of the testing but also the quality assurance of software. Mutation testing is considered as the usable, automatic and very effective technique in detecting mistakes of the set of test cases such as missing test cases, redundant test cases···  However, when using the mutation testing technique in practice, the generation of a large number of mutants has led to very high computational costs. This raises the question of whether we can reliably and accurately predict this mutation score without running mutants or not. If we can do this, it will save a lot of time and effort but still ensure the effectiveness of mutation testing. In this paper, we propose the approach using machine learning to perform mutation score cross-prediction for software which are new and completely different from the software used to generate test data (mutants) in model training and testing. The experimental results have shown that our proposed approach has achieved the positive results and is highly feasible. Thus, we believe that the approach can be applied to significantly reduce the cost of mutation testing.vi_VN
dc.language.isoenvi_VN
dc.publisherJournal of Information and Telecommunicationvi_VN
dc.subjectSoftware testingvi_VN
dc.subjectmutation testingvi_VN
dc.subjecthigher order mutation testingvi_VN
dc.subjectmutation score predictionvi_VN
dc.subjectmachine learningvi_VN
dc.titlePredicting higher order mutation score based on machine learningvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:NĂM 2023

Files in This Item:

 Sign in to read



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