Reducing the Cost of Regression Testing by Identifying Irreplaceable Test Cases
Chu-Ti Lin, Kai-Wei Tang, Cheng-Ding Chen, Gregory M. Kapfhammer
Abstract
Chu-Ti Lin, Kai-Wei Tang, Cheng-Ding Chen, Gregory M. Kapfhammer
Abstract
Test suite reduction techniques decrease the cost of software testing by removing the redundant test cases from the test suite while still producing a reduced set of tests that yields the same level of code coverage as the original suite. Most of the existing approaches to reduction aim to decrease the size of the test suite. Yet, the difference in the execution cost of the tests is often significant and it may be costly to use a test suite consisting of a few long-running test cases. Thus, this paper proposes an algorithm, based on the concept of test irreplaceability, which creates a reduced test suite with a decreased execution cost. Leveraging widely used benchmark programs, the empirical study shows that, in comparison to existing techniques, the presented algorithm is the most effective at reducing the cost of running a test suite.
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Test suite reduction techniques decrease the cost of software testing by removing the redundant test cases from the test suite while still producing a reduced set of tests that yields the same level of code coverage as the original suite. Most of the existing approaches to reduction aim to decrease the size of the test suite. Yet, the difference in the execution cost of the tests is often significant and it may be costly to use a test suite consisting of a few long-running test cases. Thus, this paper proposes an algorithm, based on the concept of test irreplaceability, which creates a reduced test suite with a decreased execution cost. Leveraging widely used benchmark programs, the empirical study shows that, in comparison to existing techniques, the presented algorithm is the most effective at reducing the cost of running a test suite.
Key concepts: Test suite, Regression testing, Computer science, Suite, Benchmark (surveying), Code coverage, Test case, Model-based testing