Anas Abuljadayel

Work place: Department of Software Engineering, Hashemite University, Zarka 13315, Jordan



Research Interests: Computational Science and Engineering, Software Construction, Software Development Process, Software Engineering


Anas S. Abuljadayel received his Masters degree in Software Engineering from the Hashemite University, Jordan in 2016. He completed his bachelor's degree in Software Engineering from The Hashemite University, Jordan in 2012. His research interest is in Software Engineering with focus on Software Testing and Mutation Testing.

Author Articles
An Approach for the Generation of Higher Order Mutants Using Genetic Algorithms

By Anas Abuljadayel Fadi Wedyan

DOI:, Pub. Date: 8 Jan. 2018

Mutation testing is a structural testing technique in which the effectiveness of a test suite is measured by the suite ability to detect seeded faults. One fault is seeded into a copy of the program, called mutant, leading to a large number of mutants with a high cost of compiling and running the test suite against the mutants. Moreover, many of the mutants produce the same output as the original program (called equivalent mutants), such mutants need to be minimized to produce accurate results. Higher order mutation testing aims at solving these problems by allowing more than one fault to be seeded in the mutant. Recent work in higher order mutation show promising result in reducing the cost of mutation testing and increasing the approach effectiveness. In this paper, we present an approach for generating higher order mutants using a genetic algorithm. The aim of the proposed approach is to produce subtle and harder to kill mutants, and reduce the percentage of produced equivalent mutants. A Java tool has been developed, called HOMJava (Higher Order Mutation for Java), which implements the proposed approach. An experimental study was performed to evaluate the effectiveness of the proposed approach. The results show that the approach was able to produce subtle higher order mutants, the fitness of mutants improved by almost 99% compared with the first order mutants used in the experiment. The percentage of produced equivalent mutants was about 4%.

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