Ahmad Raad Raheem

Work place: Department of Computer Science and Engineering, Acharya Nagarjuna Engineering, A.P, India

E-mail: ahmedraadr@yahoo.com

Website:

Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computer Architecture and Organization, Data Structures and Algorithms

Biography

Ahmed Raad Raheem received his B.Sc. from university of diyala, Iraq 2003-2007 and M.sc from Sam Higginbottom University of Agriculture, Technology and Sciences, India 2010- 2012, Research Scholar in Department of Computer Science and Engineering, Acharya Nagarjuna Engineering, A.P, India.

Author Articles
Software Reliability Growth Models with Exponentiated-gompertz Testing Effort and Release Time Determination

By Ahmad Raad Raheem Shaheda Akthar

DOI: https://doi.org/10.5815/ijcnis.2023.01.07, Pub. Date: 8 Feb. 2023

Quality is a consequential factor for the software product. During the software development at most care was taken at each step for the quality product. Development process generally embedded with several qualitative and quantitative techniques. The characteristics of final software product should reach all the standards. Reliability is a paramount element which quantifications the probability that a software product could able to work afore it authentically fails to perform its intended functionality. Software testing is paramount phase where gargantuan resources were consumed. Over around fifty percent of cost was consumed during this testing phase, that is why testing was performed in disciplined environment. Software product release time is considered to be crucial subject at which the software product testing was stopped and it could be release into market, such that the software product should have quality and reliability. In this paper we have investigated the concept of software testing effort dependent software reliability growth models by considering the exponentiated-gompertz function as testing effort function to determine the release time of the software. Thus, constructed testing effort dependent models was computed on three authentic time datasets. Parameter estimation is done through least square estimation and metrics like Mean square Error (MSE) and Absolute Error (AE) are utilized for model comparison. The proposed testing effort dependent model performance was better than the rest of the models.
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