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Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Research Article

Automation of Data Flow Class Testing Using Hybrid Evolutionary Algorithms

Author(s): Neetu Jain* and Rabins Porwal*

Volume 14, Issue 1, 2021

Published on: 08 April, 2019

Page: [317 - 330] Pages: 14

DOI: 10.2174/2213275912666190408105311

Price: $65

Abstract

Background: Software testing is a time consuming and costly process. Recent advances in complexity of software have gained attention among researchers towards the automation of generation of test data.

Objective: This paper focuses on the structural testing of object oriented paradigm based software and proposes a hybrid approach to automate the class testing applying heuristic algorithms.

Methods: The proposed algorithm performs data flow testing of classes applying all defuses adequacy criteria by automatically generating test cases. A nested 2-step methodology is applied using meta-heuristic genetic algorithm and its two variant (GA-variant1 and Ga-variant2) to produce optimized method sequences.

Results: An experiment is performed applying proposed algorithm on six test classes. The results suggest that proposed approach with GA-variant1 is better than other techniques in terms of Average d-u coverage and Average iterations.

Keywords: Test data generation, automatic class testing, data flow testing, genetic algorithm, search-based testing, control flow graph.

Graphical Abstract

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