Neural-network based test cases generation using genetic algorithm

A key issue in black-box testing is how to select adequate test cases from input domain on the basis of specification. However for some kinds of software, developing test cases from output domain is more suitable than from input domain. In this paper we present a novel approach to automatically generate test cases from output domain. A model is created via neural network to take as a function substitute for the software under test, and then on the basis of the created function model, for given outputs we employ an improved genetic algorithm to find the corresponding inputs, so that the automation of test cases generation from output domain is completed. In order to investigate the effectiveness of the approach, a number of experiments have been conducted on two different software programs under test. Experimental results show that this approach is promising and effective.