Abstract
Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.
Highlights
The software contains a set of statements that provide instructions for a computer to perform a task.These statements are divided into sequence or control statements that are executed in order
We investigated the performance of an multiple-searching genetic algorithm (MSGA) in software testing by considering the configuration of the important parameters of population size, crossover operator, and mutation operator
Every new test case of each generation called Gcov to count the number of times each decision statement in the software under test (SUT) was executed
Summary
The software contains a set of statements that provide instructions for a computer to perform a task. These statements are divided into sequence or control statements that are executed in order. The statements perform different statements depending on conditions. Control statements act to implement different sets of statements depending on if the initial condition is true or false. Control statements affect software outputs and can be the source of error. Software testing processes help verify mistakes that may happen in control statements by determining a set of inputs for a test, called test case, which can reveal the error or execute as many statements as possible. There are many software testing techniques, including genetic algorithms (GAs), particle swarm optimization, and ant colony optimization, all of which are applied to determine a set of inputs or generate test cases
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