SOFTWARE TEST DATA GENERATION USING EVOLUTIONARY TECHNIQUES

Abstract

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. One of the automation techniques is using Genetic Algorithms (GA). They are iterative algorithms that apply basic operations repeatedly in greed for optimal solutions, or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to an optimal solution. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG_GA (Test Case Generation using Genetic Algorithms) that searches for test data in a discontinuous space.