Incorporating unsupervised machine learning technique on genetic algorithm for test case optimization

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

[1]  Chayanika Sharma,et al.  Applying Genetic Algorithm for Prioritization of Test Case Scenarios Derived from UML Diagrams , 2014, ArXiv.

[2]  Sureswaran Ramadass,et al.  Employing machine learning algorithms to detect unknown scanning and email worms , 2014, Int. Arab J. Inf. Technol..

[3]  Ladan Tahvildari,et al.  Size-Constrained Regression Test Case Selection Using Multicriteria Optimization , 2012, IEEE Transactions on Software Engineering.

[4]  Abhishek Singhal,et al.  A NOVEL APPROACH FOR PRIORTIZATION OF OPTIMIZED TEST CASES , 2012 .

[5]  Mark Harman,et al.  Clustering test cases to achieve effective and scalable prioritisation incorporating expert knowledge , 2009, ISSTA.

[6]  Lilly Raamesh.  An Efficient Reduction Method for Test Cases , 2010 .

[7]  Gregg Rothermel,et al.  On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques , 2006, IEEE Transactions on Software Engineering.

[8]  Nadine Mandran,et al.  Prioritizing test cases with string distances , 2011, Automated Software Engineering.

[9]  G. N. Purohit,et al.  Test case prioritization techniques “an empirical study” , 2014, 2014 International Conference on High Performance Computing and Applications (ICHPCA).

[10]  A. K. Misra,et al.  Prioritizing Test Suites Using Clustering Approach in Software Testing , 2012 .

[11]  Vivek Shrivastava,et al.  Early fault detection model using integrated and cost-effective test case prioritization , 2011, Int. J. Syst. Assur. Eng. Manag..

[12]  Ahmed S. Ghiduk.  Automatic Generation of Object-Oriented Tests with a Multistage-Based Genetic Algorithm , 2010, J. Comput..

[13]  Anon Sukstrienwong,et al.  Solving multiobjective optimization under b ounds by genetic algorithms , 2011 .

[14]  Anne M. Denton,et al.  A clustering approach to improving test case prioritization: An industrial case study , 2011, 2011 27th IEEE International Conference on Software Maintenance (ICSM).