A Survey on Software Testing Techniques using Genetic Algorithm

TL;DR

This paper surveys the application of Genetic Algorithms in software testing, focusing on automating test case generation and prioritization to improve efficiency and effectiveness.

Contribution

It provides a comprehensive overview of how Genetic Algorithms are used to address key challenges in software testing processes.

Findings

Genetic Algorithms enhance test case generation efficiency.

GA-based methods improve test prioritization accuracy.

The survey highlights research trends and future directions in GA-driven testing.

Abstract

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.