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 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.