Page 104 - أبحاث كلية تكنولوجيا المعلومات 2016-2020

2020 Publications

Intelligent Automation And Soft Computing

2020 Vol. 26, no. 4, 725–733

The Genetic Algorithm and Binary Search Technique in the Program Path
Coverage for Improving Software Testing Using Big Data

Aysh Alhroob, Wael Alzyadat, Ayad Tareq Imam, Ghaith M. Jaradat
Software program testing is the procedure of exercising a software component with a selected set of
test cases as a way to discover defects and assess quality. Using software testing automation,
especially the generating of testing data increases the effectiveness and efficiency of software testing
as a whole. Instead of creating testing data from scratch, Big Data (BD) offers an important source of
testing data. Although it is a good source, there is a need to select a proper set of testing data for the
sake of selecting an optimal sub-domain input values from the BD. To refine the efficiency of
software testing, this paper proposes a hybrid Genetic Algorithm and Binary Search (BSGA)
technique that is used for detecting the error-prone path in a program. The BSGA combines the
Genetic Algorithm (GA) with the Binary Search (BS) algorithm that uses the BD as input values for
the program path coverage, and thus enhances the software testing. The BSGA represents a robust
nonlinear search technique and a better quality solution, which therefore results in a cost reduction in
the software testing industry. The experiments show that the results approved the impact of using the
BS to enhance the performance of the GA, in terms of finding optimal test cases and test data for the
input Big Data domain values. Whereas, these results minimize the cost of testing.
https://www.researchgate.net/profile/Ayad_Imam/publication/339587787_The_Genetic_Algorithm_a
nd_Binary_Search_Technique_in_the_Program_Path_Coverage_for_Improving_Software_Testing_U
sing_Big_Data/links/5f79fd5e458515b7cf662e3f/The-Genetic-Algorithm-and-Binary-Search-
Technique-in-the-Program-Path-Coverage-for-Improving-Software-Testing-Using-Big-Data.pdf

83