Shahram Moadab

Work place: Department of Electrical, IT and Computer Sciences, Science and Research Branch, Islamic Azad University, Qazvin, Iran

E-mail: moadabsh@gmail.com

Website:

Research Interests: Software Creation and Management, Software Design, Computer systems and computational processes, Computer Architecture and Organization, Data Structures and Algorithms, Algorithm Design

Biography

Shahram Moadab is a Lecturer of Computer Engineering at the University of Guilan, Iran. He received his B.Sc. degree in Computer Engineering in 2003 from Azad University, Lahijan Branch, Iran. He also earned from Azad University, Qazvin Branch, Iran, his M.Sc. degree in Computer Engineering in 2012. His experience in teaching extends to 12 years. He has published several papers in internationals conferences and Journals. His research interests include Software Testing and Algorithm Design.

Author Articles
Diagnostic Path-Oriented Test Data Generation by Hyper Cuboids

By Shahram Moadab Mohsen falh rad

DOI: https://doi.org/10.5815/ijieeb.2014.06.01, Pub. Date: 8 Dec. 2014

One of the ways of test data generation is using the path-oriented (path-wise) test data generator. This generator takes the program code and test adequacy criteria as input and generates the test data in order to satisfy the adequacy criteria. One of the problems of this generator in test data generation is the lack of attention to generating the diagnostic test data. In this paper a new approach has been proposed for path-oriented test data generation and by utilizing it, test data is generated automatically realizing the goal of discovering more important errors in the least amount of time. Since that some of the instructions of any programming language are more error-prone, so the paths that contain these instructions are selected for perform test data generation process. Then, the input domains of these paths variables are divided by a divide-and-conquer algorithm to the subdomains. A set of different subdomains is called hypercuboids, and test data will be generated by these hypercuboids. We apply our method in some programs, and compare it with some previous methods. Experimental results show proposed approach outperforms same previous approaches.

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