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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| 主要な著者: | , , , , , |
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| フォーマット: | 論文 |
| 出版事項: |
Excelligent Academia
2022
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/104738/ https://excelligentacademia.com/journal/index.php/AICR/article/view/76 |
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| 要約: | 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. |
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