Abstract:
The success of a project depends on the efficient prioritisation of its software requirements. The
application of clustering and related data mining techniques for requirements prioritisation
within the context of software engineering is still unexplored and frequently overshadowed by
established procedures. This study begins a thorough investigation of clustering's untapped
potential as a cutting-edge method to enhance requirements prioritisation and enhance project
outcomes. To improve the organisation of complicated requirements and determine their relative
importance, the study offers the novel idea of combining clustering techniques with the Analytic
Hierarchy Process (AHP). Two meticulously constructed quantitative datasets, each containing
20 and 100 software meticulously form the core of this research. Notably, the development of an
AHP dataset represents a fresh contribution and serves as a standard by which clustering
methods can be unbiasedly assessed. Five main clustering algorithms emerge as the investigation
progresses: K-means, Hierarchical, Partition Around Medoids (PAM), Gaussian Mixture Models
(GMM), and BIRCH. Each of these methods offers a wide range of analytical techniques for
examining the datasets. The Dunn Index, Silhouette Index, and Calinski Harabaz Index are used
to statistically measure the quality and cohesion of the created clusters to assess the effectiveness
of these approaches. The MoSCoW approach is then used to order the identified criteria into
clusters, guaranteeing that crucial requirements are met while allowing for flexibility for less
important features. This dual strategy combines strategic prioritisation with quantitative analysis,
allowing for an unbiased evaluation of clustering results and simplifying resource allocation
based on requirement priority. Overall, this research pioneers the innovative integration of
advanced data analysis methodologies into project management and emphasises the viability of
clustering techniques for requirement prioritisation in the software domain, with a focus on the
ground-breaking combination of AHP and clustering as a transformative approach to prioritise
requirements.
