Automation of data flow class testing using hybrid evolutionary algorithms

被引：0

作者：

Jain N. ^{[1
]}

Porwal R. ^{[2
]}

机构：

[1] Department of Computer Application, Faculty of System Sciences, Mewar University, Chittorgarh

[2] Department of Information Technology, Lal Bahadur Shastri Institute of Management, New Delhi

来源：

Recent Advances in Computer Science and Communications | 2021年 / 14卷 / 01期

关键词：

Automatic class testing; Control flow graph; Data flow testing; Genetic algorithm; Search-based testing; Test data generation;

D O I：

10.2174/2213275912666190408105311

中图分类号：

学科分类号：

摘要：

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. © 2021 Bentham Science Publishers.

引用

页码：317 / 330

页数：13

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