Incorporating Unsupervised Machine Learning Technique
on Genetic Algorithm for Test Case Optimization
Maragathavalli
Palanivel and Kanmani Selvadurai
Department of Information
Technology, Pondicherry Engineering College, India
Abstract: Search-based
software testing uses random or directed search techniques to address problems.
This paper discusses on test case selection and prioritization by combining
genetic and clustering algorithms. Test cases have been generated using genetic
algorithm and the prioritization is performed using group-wise clustering
algorithm by assigning priorities to the generated test cases thereby reducing
the size of a test suite. Test case selection is performed to select a suitable
test case in order to their importance with respect to test goals. The
objectives considered for criteria-based optimization are to optimize test
suite with better condition coverage and to improve the fault detection
capability and to minimize the execution time. Experimental results show that
significant improvement when compared to the existing clustering technique in
terms of condition coverage up to 93%, improved fault detection capability achieved
upto 85.7% with minimal execution time of 4100ms.
Keywords: Test case selection and prioritization,
group-wise clustering.
Received August 14, 2014; accepted August 31, 2015
|
|