A Path-Oriented Test Data Generation Approach Hybridizing Genetic Algorithm and Artificial Immune System

被引：0

作者：

Bhattacharjee, Gargi ^{[1
]}

Saluja, Ashish Singh ^{[1
]}

机构：

[1] Veer Surendra Sai Univ Technol, Dept IT, Burla 768018, Odisha, India

来源：

COMPUTATIONAL INTELLIGENCE IN DATA MINING | 2019年 / 711卷

关键词：

Test data generation; Genetic Algorithm; Artificial Immune System; Path coverage; White box testing; Software testing; Test cases; Evolutionary Computing; GeMune Algorithm;

D O I：

10.1007/978-981-10-8055-5_58

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Validating the correctness of software through a tool has started gaining a wide foothold in the business. A test data generator is one such tool which automatically generates the test data for software so as to attain maximum coverage. Researchers in the past have adopted different evolutionary algorithms to automatically generate a data set. One such often used procedure is Genetic Algorithm (GA). Due to certain flaws present in this approach, we have redefined the cause of concern for coverage in structural testing. In this paper, we have explored the properties of immune system along with GA. We have proposed a new hybrid algorithm-GeMune algorithm-inspired from these biological backdrops. Experimental results certify that the new algorithm has a better coverage compared to the use of only Genetic Algorithm for structural testing.

引用

页码：649 / 658

页数：10

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