 Software effort estimation using cascade neural network optimised based on modified particle swarm optimisation (MPSO-CNN)Mohammed Abdulmajeed Moharram, Saurabh Bilgaiyan and Santwana Sagnika International Journal of Productivity and Quality Management, 2025, vol. 44, issue 1, 40-70 Abstract: Software effort estimation has a significant role in software development engineering. The inaccurate estimation will increase the failure possibilities of the project. On the contrary, accurate estimation enables the project developers to finalise the projects within the required time and budget. Furthermore, it is considered a big challenge to obtain the satisfactory accuracy of project development at the beginning. To tackle this problem, soft computing techniques such as artificial neural network (ANN) has already demonstrated a remarkable performance in software effort estimation. However, the optimal weights for the neural network are still considered a big dilemma. In this paper, a cascade neural network (CNN) is optimised based on modified particle swarm optimisation (PSO). The modified PSO can overcome the premature convergence of PSO as well as avoid falling into local optima effectively. The experimental results have shown the superiority of the proposed work compared with the standard PSO significantly. Keywords: particle swarm optimisation; PSO; cascade neural network; CNN; Pearson correlation; standard deviation; effort estimation. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpqma:v:44:y:2025:i:1:p:40-70 Access Statistics for this article More articles in International Journal of Productivity and Quality Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.