Assisted Requirements Selection by Clustering using an Analytical Hierarchical Process

This research investigates the fusion of the Analytic Hierarchy Process (AHP) with clustering techniques to enhance project outcomes. Two quantitative datasets comprising 20 and 100 software requirements are analyzed. A novel AHP dataset is developed to impartially evaluate clustering strategies. Fi...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications 2024, Vol.15 (4)
Main Authors: Saleem, Shehzadi Nazeeha, Mohaisen, Linda
Format: Article
Language:English
Subjects:
Algorithms
Analytic hierarchy process
Clustering
Computer science
Convergence
Data analysis
Datasets
Decision making
Hierarchies
Information technology
Numerical analysis
Project management
Quantitative analysis
Resource allocation
Software development
Software engineering
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This research investigates the fusion of the Analytic Hierarchy Process (AHP) with clustering techniques to enhance project outcomes. Two quantitative datasets comprising 20 and 100 software requirements are analyzed. A novel AHP dataset is developed to impartially evaluate clustering strategies. Five clustering algorithms (K-means, Hierarchical, PAM, GMM, BIRCH) are employed, providing diverse analytical tools. Cluster quality and coherence are assessed using evaluation criteria including the Dunn Index, Silhouette Index, and Calinski Harabaz Index. The MoSCoW technique organizes requirements into clusters, prioritizing critical requirements. This strategy combines strategic prioritization with quantitative analysis, facilitating objective evaluation of clustering results and resource allocation based on requirement priority. The study demonstrates how clustering can prioritize software requirements and integrate advanced data analysis into project management, showcasing the transformative potential of converging AHP with clustering in software engineering.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0150403