Testing Method for Software With Randomness Using Genetic Algorithm

Abstract

The main task of software testing is the generation of test data with high quality in restricted time. But for software with uncertainties, such as randomness, existing methods of generating test data often lose their effectiveness. For software with randomness, a test datum might cover the test target in one run, but obtains different results in the next run. Therefore, special testing methods must be exploited for this kind of software. In order to test software with randomness, we first propose a novel test adequacy criterion, and then build a mathematical model for generating test data according to the new criterion. Finally, we present a method of solving the optimization model using a set-based genetic algorithm. We apply the proposed method to test 12 programs, and compare with traditional genetic algorithm and the random method. From the experimental results we can see that, the proposed adequacy criterion is available for the software with randomness and the proposed algorithm can effectively generate test data.

Multi-Task Optimization-Based Test Data Generation for Mutation Testing via Relevance of Mutant Branch and Input Variable

“…Software testing, aiming to find faults in a software product, is one of the vital processes in the life cycle of software development, which generally consists of two phases, i.e., generating test data and executing the software product with the generated test data [1], [2]. Among various testing methods, Mutation testing is a fault-based software testing technique, which is proposed by Hamlet et al [3] and DeMillo et al [4].…”