Effective Generation of Test Cases Using Genetic Algorithms and Optimization Theory

Abstract: In software projects, one of the main challenges and sources of success or failure is the effective use of available resources. Using effective techniques in regression testing is important to reduce the amount of required resources. This is accomplished through reducing the number of executed test cases without affecting coverage. In this research, genetic algorithms and optimization theory concepts are applied on test case generation and reduction optimization. The methods start by generating an initial pool of test cases through selecting valid paths in the GUI graph that is generated from the tested software dynamically using an in-house developed tool. The selected test cases are then improved through measuring and evaluating fitness functions. The two fitness functions used in this research were the test set generation speed and the test set coverage. Optimization theory is also used to find the best set, measured according to a particular fitness function that can best represent the whole testing database while preserving all other constraints