A review on search-based mutation testing
Big Data is a larger and more complex collection of datasets that exceeds the processing. In order to improve the productivity of non-testable Big Data, machine learning is able to determine various types of high volume, velocity and variety of data that need to be processed. Search-based mutation t...
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Excelligent Academia
2022
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my.utm.1047382024-02-25T04:53:47Z http://eprints.utm.my/104738/ A review on search-based mutation testing Abdul Rahman, Nor Ashila Hassan, Rohayanti Ahmad, Johanna Zakaria, Noor Hidayah Sim, Hiew Moi Sa'adon, Nor Azizah QA75 Electronic computers. Computer science Big Data is a larger and more complex collection of datasets that exceeds the processing. In order to improve the productivity of non-testable Big Data, machine learning is able to determine various types of high volume, velocity and variety of data that need to be processed. Search-based mutation testing works by formulating the test data generation/optimization and mutant optimization problems as search problems and by applying meta-heuristic techniques to solve them. This paper aims to present the researches carried out in mutation testing particularly in search-based approaches. 205 papers were reviewed and analyzed from 2014-2018. This paper later on proceeds to elaborate on SBMT functions, First and Higher Order Mutant as well as multi-objective optimization. Excelligent Academia 2022 Article PeerReviewed Abdul Rahman, Nor Ashila and Hassan, Rohayanti and Ahmad, Johanna and Zakaria, Noor Hidayah and Sim, Hiew Moi and Sa'adon, Nor Azizah (2022) A review on search-based mutation testing. Academia of Information Computing Research, 3 (1). pp. 1-9. ISSN 2716-6465 https://excelligentacademia.com/journal/index.php/AICR/article/view/76 NA |
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QA75 Electronic computers. Computer science Abdul Rahman, Nor Ashila Hassan, Rohayanti Ahmad, Johanna Zakaria, Noor Hidayah Sim, Hiew Moi Sa'adon, Nor Azizah A review on search-based mutation testing |
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Big Data is a larger and more complex collection of datasets that exceeds the processing. In order to improve the productivity of non-testable Big Data, machine learning is able to determine various types of high volume, velocity and variety of data that need to be processed. Search-based mutation testing works by formulating the test data generation/optimization and mutant optimization problems as search problems and by applying meta-heuristic techniques to solve them. This paper aims to present the researches carried out in mutation testing particularly in search-based approaches. 205 papers were reviewed and analyzed from 2014-2018. This paper later on proceeds to elaborate on SBMT functions, First and Higher Order Mutant as well as multi-objective optimization. |
| format |
Article |
| author |
Abdul Rahman, Nor Ashila Hassan, Rohayanti Ahmad, Johanna Zakaria, Noor Hidayah Sim, Hiew Moi Sa'adon, Nor Azizah |
| author_facet |
Abdul Rahman, Nor Ashila Hassan, Rohayanti Ahmad, Johanna Zakaria, Noor Hidayah Sim, Hiew Moi Sa'adon, Nor Azizah |
| author_sort |
Abdul Rahman, Nor Ashila |
| title |
A review on search-based mutation testing |
| title_short |
A review on search-based mutation testing |
| title_full |
A review on search-based mutation testing |
| title_fullStr |
A review on search-based mutation testing |
| title_full_unstemmed |
A review on search-based mutation testing |
| title_sort |
review on search-based mutation testing |
| publisher |
Excelligent Academia |
| publishDate |
2022 |
| url |
http://eprints.utm.my/104738/ https://excelligentacademia.com/journal/index.php/AICR/article/view/76 |
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1792148009941729280 |
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13.223943 |