To cope with this issue, different techniques have been proposed to improve cost-effectiveness of test suites, such as Test Suite Minimization, also called test suite reduction, where redundant test cases are permanently removed; and Test Case Selection, which selects test cases based on changed parts of a System Under Test (SUT) (Yoo and Harman 2012).
Other works focus on test case prioritization and selection, integration testing, selection of testing strategies, test case allocation and test case selection and evaluation.
The general test case selection problem is concerned with selecting a subset of the test cases according to a specific (stop) criterion, whereas test suite reduction techniques focus on selecting a subset of the test cases, but the selected subset must provide the same requirement coverage as the original suite (Harrold et al. 1993).
Techniques for ordering the test cases may be required to support test case selection, for instance, to address constrained costs of running and analyzing the complete test suite and to improve the rate of failure detection.
While existing techniques for test case selection provide methods to limit the growth of the number of test cases with software evolution, many of them are based on the assumption that source code analysis is available.
The task of reducing a test suite based on a selection criterion is known as test case selection.
Fig. 12 α-metric variations of the multi-RAT UE AP selection test case Fig. 13 β-metric variations of the multi-RAT UE AP selection test case.
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.
Very few works have explored the problem of test case selection given the constraint that source code analysis is not available.
The multi-RAT UE AP selection test case considers a UE connected to two separate AP.
