A Technique for Generating Test Data Using Genetic Algorithm

Abstract

Automatic test data generation for path coverage is an undecidable problem and genetic algorithm (GA) has been used as one good solution. This paper presents a method for optimizing GA efficiency by identifying the most critical path clusters in a program under test. We do this by using the static program analysis to find all the paths having the path conditions with low probability in generating coverage data, then basing on these path conditions to adjust the procedure of generating new populations in GA. The proposed approach is also applied some program under tests. Experimental results show that improved GA which can generate suitable test data has higher path coverage than the traditional GA.