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Volume 14, Issue 2 (6-2022)
itrc 2022, 14(2): 32-40 |
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Kashefi Gargari S, Keyvanpour M R. Comparative Analytical Survey on SBST Challenges from the Perspective of the Test Techniques. itrc 2022; 14 (2) :32-40
URL: http://journal.itrc.ac.ir/article-1-499-en.html
URL: http://journal.itrc.ac.ir/article-1-499-en.html
1- Department of Computer Engineering Faculty of Engineering Alzahra University Tehran, Iran
2- Department of Computer Engineering Faculty of Engineering Alzahra University Tehran, Iran ,keyvanpour@alzahra.ac.ir
2- Department of Computer Engineering Faculty of Engineering Alzahra University Tehran, Iran ,
Abstract: (1841 Views)
Despite several decades of working on software quality assurance methods, they still require further investigation. It is paramount to detect a process detecting possible software errors with a minimum budget and effort. Search-based software testing (SBST) is an approach to automated software testing that aims to find a suitable alternative to manual testing. The SBST is to transform the test problem into an optimization problem and obtain better solutions by searching the problem space. Search-based testing has some disadvantages and advantages. The novelty of this paper is that, besides representing the significance and efficiency of SBST in software testing, the search-based test challenges were detected and described from the perspective of the test techniques. Our work is to extract challenges from reliable sources and research and their classification based on test techniques. For this purpose, we considered this framework: 1) A systematic introduction to the most critical metaheuristic optimization algorithms. 2) classifying the test techniques and explaining their advantages and disadvantages. 3) proposing a suitable classification for the challenges of the search-based test area based on the technique used. Our motivation to do this research was to provide complete knowledge about search-based software testing challenges so that new researchers could choose their research fields with prior knowledge and provide a way to improve existing methods. Finally, the results of this paper can be used to compare the existing test techniques used in SBST, select the best one, and represent the challenges of each technique.
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