<![CDATA[Among algorithmic-based software development effort estimation frameworks available, Use Case Points is one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of use case. This study investigates the potential of integrating particle swarm optimization with the Use Case Points framework, where PSO is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The accuracy and performance evaluation of the proposed model is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant.]]>
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