@Article{iasc.2020.010106,
AUTHOR = {Aysh Alhroob, Wael Alzyadat, Ayad Tareq Imam, Ghaith M. Jaradat},
TITLE = {The Genetic Algorithm and Binary Search Technique in the Program Path  Coverage for Improving Software Testing Using Big Data},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {725--733},
URL = {http://www.techscience.com/iasc/v26n4/40276},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.32604/iasc.2020.010106}
}