A Review of Random Test Case Generation using Genetic Algorithm

Deepti Bala Mishra; Saurabh Bilgaiyan; Rajashree Mishra; Arup Abhinna Acharya  and Samaresh Mishra

doi:10.17485/ijst/2017/v10i30/107654

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A Review of Random Test Case Generation using Genetic Algorithm

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2017/v10i30/107654

Year: 2017, Volume: 10, Issue: 30, Pages: 1-7

Original Article

A Review of Random Test Case Generation using Genetic Algorithm

Deepti Bala Mishra¹ , Saurabh Bilgaiyan¹ , Rajashree Mishra² , Arup Abhinna Acharya¹ and Samaresh Mishra¹

¹School of Computer Engineering, KIIT University, Bhubaneswar – 751024, Odisha, India; [email protected], [email protected], [email protected], [email protected] ²School of Applied Sciences, KIIT University, Bhubaneswar – 751024, Odisha, India; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: This research paper presents how Genetic algorithm is efficiently used in random test case generation during functional software testing. Methods/Statistical Analysis: Different hybridized Genetic Algorithms are used to generate test data automatically and optimized those test cases to solve many complex problem related to software testing. Findings: Genetic Algorithms are successfully used in software testing with increasing number of test case generation and provides a means of an automatic test case generator. Applications/Improvements: This study gives us a brief idea to implement Genetic Algorithms in software testing for optimum results and also it can be used with the neural networks and fuzzy systems for different types of testing to improve the performance.

Keywords: Black-Box Testing, Fitness Function, Genetic Algorithm (GA), Neural Network, Software Testing, Test case Generation