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. 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.

@article{Saleem2024,
  title     = {Assisted Requirements Selection by Clustering using an Analytical Hierarchical Process},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Shehzadi Nazeeha Saleem and Linda Mohaisen},
  doi       = {10.14569/IJACSA.2024.0150403},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150403}
}