An Efficient Approach to Test Suite Minimization for 100% Decision Coverage Criteria using K-Means Clustering Approach
Fayaz Ahmad Khan, Anil Gupta, Dibya Jyoti Bora · 2015
Test case generation is one of the labour- intensive processes in softer testing. Test case generation has a very strong impact on the effectiveness and efficiency of the software under test. Thus, it is imperative to reduce cost and improve the effectiveness of software testing by automatic the test case generation techniques. But, both manual and automation test case generation techniques generate large and redundant test cases that are infeasible to be considered for practical execution. Thus an efficient approach is needed which can limit the size and redundancy of test suite based on certain code coverage criteria like statement coverage, branch/ decision coverage and dataflow criteria. In this study, K-Means clustering approach is implemented on a test suite which is large in size and contains redundant test cases, in order to reduce the size of test cases to an effective number of test cases for 100% decision coverage of a code.