Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

(1) * Ardiansyah Ardiansyah Mail (Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta | Department of Informatics, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
(2) Ridi Ferdiana Mail (Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia)
(3) Adhistya Erna Permanasari Mail (Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia)
*corresponding author


Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm 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 proposed model’s accuracy and performance evaluation metric 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. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. 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.


Use case points; Effort estimation; Particle swarm; Metaheuristic optimizationUse case complexity; optimization;




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