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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Shaheen, Ameen
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Computer Science & IT

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Incremental prioritization using an iterative model for smallscale systems Shaheen, Ameen; Alzyadat, Wael; Alhroob, Aysh; Asfour, A. Nasser
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp565-574

Abstract

To improve customer satisfaction during the requirement engineering process and create higher consistency in the developed software, there is a growing trend toward the development and delivery of software in an incremental manner. This paper introduces a novel approach to prioritizing the initial development of core subsystems. This prioritization ensures that the most critical subsystems, which contribute significantly to the project’s overall success, are addressed first. Our method involves employing an incremental model with iterative modeling, where each subsystem is assigned a profitability score ranging from 1 to 10. The iterative model is then utilized to identify the most suitable subsystem for the next development stage. The results of our study indicate that utilizing the total profit weight in conjunction with the iterative model effectively identifies the central subsystem of the entire project. This approach proves to be the optimal starting point for development, helping streamline the process and contribute to a more efficient software delivery strategy.
Exploring diverse perspectives: enhancing black box testing through machine learning techniques Nafez Jalal, Heba; Alhroob, Aysh; Shaheen, Ameen; Alzyadat, Wael
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp238-246

Abstract

Black box testing plays a crucial role in software development, ensuring system reliability and functionality. However, its effectiveness is often hindered by the sheer volume and complexity of big data, making it difficult to prioritize critical test cases efficiently. Traditional testing methods struggle with scalability, leading to excessive resource consumption and prolonged testing cycles. This study presents an AI-driven test case prioritization (TCP) approach, integrating decision trees and genetic algorithms (GA) to optimize selection, eliminate redundancy, and enhance computational efficiency. Experimental results demonstrate a 96% accuracy rate and a 90% success rate in identifying relevant test cases, significantly improving testing efficiency. These findings contribute to advancing automated software testing methodologies, offering a scalable and efficient solution for handling large-scale, data-intensive testing environments.
Co-Authors Alhroob, Aysh Alzyadat, Wael Asfour, A. Nasser Nafez Jalal, Heba