Dynamic Test Data Generation using Negative Selection Algorithm and Equivalence Class Partitioning

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Wasiur Rhmann
Dr. Gufran Ahmad Ansari

Abstract

Test data generation is challenging in software testing. Huge amount of generated test data is used to expose faults in the software. Test data generated inefficiently leads extra efforts. In path testing different independent paths generated from Control Flow Graph (CFG) are forced to traverse by test data. In the present paper, we proposed a novel technique to dynamically reduce test data based on Equivalence partitioning of output domain. Paths which are functionally meaningful are considered for test data generation. Experiments are performed on benchmark programs.

Keywords: Test data, Negative Selection Algorithm, Activity Diagram, Software Testing

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