Genetic Algorithm for Test Suite Optimization: An Experimental Investigation of Different Selection Methods

Chetan J Shingadiya Et.Al

doi:10.17762/turcomat.v12i3.1661

Abstract

Software Testing is an important aspect of the real time software development process. Software testing always assures the quality of software product. As associated with software testing, there are few very important issues where there is a need to pay attention on it in the process of software development test. These issues are generation of effective test case and test suite as well as optimization of test case and suite while doing testing of software product. The important issue is that testing time of the test case and test suite. It is very much important that after development of software product effective testing should be performed. So to overcome these issues of optimization, we have proposed new approach for test suite optimization using genetic algorithm (GA). Genetic algorithm is evolutionary in nature so it is often used for optimization of problem by researcher. In this paper, our aim is to study various selections methods like tournament selection, rank selection and roulette wheel selection and then we apply this genetic algorithm (GA) on various programs which will generate optimized test suite with parameters like fitness value of test case, test suite and take minimum amount of time for execution after certain preset generation. In this paper our main objectives as per the experimental investigation, we show that tournament selection works very fine as compared to other methods with respect fitness selection of test case and test suites, testing time of test case and test suites as well as number of requirements.

Highlights

Genetic Algorithm is evolutionary in nature which was proposed by John Holland [31]
In this paper we have proposed improved genetic algorithm (GA) with parameters like tournament selection, multipoint crossover method and bit flipped mutation method for test suite optimization
Results are clearly shows that fitness value of each individual in tournament selection is far better than other two techniques ; test sites fitness value is very fine in tournament selection other two methods

Summary

Introduction

Genetic Algorithm is evolutionary in nature which was proposed by John Holland [31]. From past many years, GA is used for optimization of problem in various fields like blueprint detection, robotics, artificial intelligence and many more. In order to use genetic algorithm inititially it required to present the chromosome solutions. To evaluate these chromosomes as solutions we have to define fitness function for the same [31]. This genetic algorithm uses idea of population and generation to evaluate through inspired in nature. As per the Darwin principles of continued existence which is called “the continued existence of the fittest”, the good chromosomes are selected while the other one die away These good chromosomes regenerate new chromosomes with fine genes which form new generation more feasible in nature. Our aim is to study various selections methods like tournament selection, rank selection and roulette wheel selection and we apply this genetic algorithm (GA) on various programs which will generate optimized test suite with parameters like fitness value of test case, test suite and take minimum amount of time for execution after certain preset generation. [32] Simple steps of genetic algorithm is given below

Objectives

Methods

Results

Conclusion