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Genetic algorithms have been successfully used in many software testing activities such as generating test data, selecting test cases and test cases prioritization.
In this work, by utilizing local and global search properties of a bat algorithm, a new bat-inspired test cases prioritization algorithm (BITCP) is proposed.
If the response system is overwhelmed by numerous cases, prioritization of cases for response can be made based on the likelihood of onward transmission, as is exemplified in Swaziland where prioritization is based on local receptivity derived from risk maps the most receptive areas being responded to first.
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Test case prioritization (TCP) has shown to improve test effectiveness.
However, few studies have discussed the problem of GUI test-case prioritization.
Finally, we examine recent developments in advanced topics such as utilization of existing tests, test case prioritization, fault localization, and evolution of combinatorial models.
Test-case prioritization has been proposed and used in recent years because it can improve the rate of fault detection during the testing phase.
To address this problem, many researchers and practitioners have proposed and empirically evaluated various regression testing techniques, such as regression test selection, test case prioritization, and test suite minimization.
In this paper, we focus on general test case prioritization.
To perform test case prioritization, historical data are utilized.
General test case prioritization - test case prioritization is applied any time in the software development process, even in the initial testing activities; Regression testing prioritization - test case prioritization techniques execute after a set of changes in the SUT.
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