Applying Consistent Fuzzy Preference Relation in Weighting Software Effort Estimation Criteria
Abstract
Software effort estimation (SEE) is a critical process in project planning, as it determines budget allocation, resource management, and timeline accuracy. The weighting of estimation criteria significantly influences the reliability of the estimation model. This study aims to determine the weights of SEE criteria using a fuzzy logic approach, specifically the Consistent Fuzzy Preference Relation (CFPR) method. As a Multi-Criteria Decision Making (MCDM) technique, CFPR offers an efficient mechanism for extracting consistent expert preferences by requiring only n-1 pairwise comparisons from n criteria, making it suitable for rapid weighting calculations. The study evaluates four main attributes: Product, Computer, Personnel, and Project. Expert assessments were conducted using crisp numbers on a 1-9 scale. The results show the following attribute weights: Product (0.372), Computer (0.275), Personnel (0.231), and Project (0.122). Furthermore, the top three ranked cost drivers are Required Reliability (0.1674), Product Complexity (0.1384), and Execution Time Constraint (0.1050). Conversely, the lowest weights were assigned to Programming Language Experience (0.0270), Virtual Machine Experience (0.0296), and Required Development Schedule (0.0303). The integration of CFPR into SEE models produces a stable and interpretable weight distribution, thereby enhancing the accuracy of effort estimation.
References
J. G. Rivera Ibarra, G. Borrego, and R. R. Palacio, “Early estimation in agile software development projects: A systematic mapping study,” Informatics, dec 2024.
A. Dogga, R. Gatte, B. Kiran Kumar, and A. Rapaka, “A synergistic approach to software effort estimation using fuzzy logic and machine learning,” Journal of Information Systems Engineering and Management, vol. 10, pp. 2468–4376, 2025.
H. Rastogi, S. Dhankhar, and M. Kakkar, “A survey on software effort estimation techniques,” International Journal of Computer Applications, pp. 826–830, 2014. Journal name implied from common indexing, original text omitted journal name.
A. Purwanto and L. P. Manik, “Software effort estimation using logarithmic fuzzy preference programming and least squares support vector machines,” Scientific Journal of Informatics, vol. 10, pp. 1–12, dec 2022.
M. Rahman, P. P. Roy, M. Ali, T. Gonc¸alves, and H. Sarwar, “Software effort estimation using machine learning technique,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 4, p. 2023, 2023.
S. Alturki and E.-A. Fazal, “Comprehensive analysis of software effort estimation techniques: Evolving trends, key challenges, and prospective directions,” 2025.
A. B. Nassif, M. Azzeh, A. Idri, and A. Abran, “Software development effort estimation using regression fuzzy models,” Computational Intelligence and Neuroscience, vol. 2019, 2019.
A. Trendowicz, J. M¨unch, and R. Jeffery, “State of the practice in software effort estimation,” in Software Project Effort Estimation, pp. 232–245, Springer, 2015.
R. Sarno, J. Sidabutar, and Sarwosri, “Improving the accuracy of cocomo’s effort estimation based on neural network and fuzzy logic model,” in International Conference on Information Communication Technology and Systems (ICTS), pp. 197–202, 2015.
P. V. G. D. P. R. C. V. Hari, “A fine parameter tuning for cocomo 81 software effort estimation using particle swarm optimization,” Journal of Software Engineering, 2011.
R. K. Yadav, “Optimized model for software effort estimation using cocomo-2 metrics with fuzzy logic,” International Journal of Advanced Research in Computer Science, vol. 8, no. 7, pp. 121–125, 2017.
C. S. Reddy and K. V. S. V. N. Raju, “An improved fuzzy approach for cocomo’s effort estimation using gaussian membership function,” Journal of Software, vol. 4, no. 5, pp. 452–459, 2009.
S. Chakraverti, S. Kumar, S. C. Agarwal, and A. K. Chakraverti, “Modified cocomo model for maintenance cost estimation of real time system software,” International Journal of Computer Science and Network, vol. 1, no. 1, pp. 2277–5420, 2012.
E. Herrera-Viedma, F. Herrera, F. Chiclana, and M. Luque, “Some issues on consistency of fuzzy preference relations,” European Journal of Operational Research, vol. 154, pp. 98–109, apr 2004.
X. Deng, F. T. S. Chan, R. Sadiq, S. Mahadevan, and Y. Deng, “D-cfpr: D numbers extended consistent fuzzy preference relations.” ArXiv, mar 2014.
T. Wahyuningrum and R. Pandiya, “Logarithmic fuzzy preference programming approach for evaluating university ranking optimization,” Scientific Journal of Informatics, vol. 4, no. 1, 2017.
Y. H. Chen and R. J. Chao, “Supplier selection using consistent fuzzy preference relations,” Expert Systems with Applications, vol. 39, pp. 3233–3240, feb 2012.
T. Wahyuningrum, A. Azhari, and Suprapto, “Improving ecommerce severity rating measurement using consistent fuzzy preference relation,” in International Conference on Soft Computing in Data Science, (Iizuka, Japan), pp. 1–12, Springer Nature, 2019.
T. C. Wang and Y. H. Chen, “Applying consistent fuzzy preference relations to partnership selection,” Omega, vol. 35, no. 4, pp. 384–388, 2007.
I. I. Lestari, N. A. Ekowati, and S. Sulistiyasni, “Descriptive analysis and comparison of reasoner using onti measures,” Jurnal Teknik Informatika (Jutif), vol. 5, pp. 301–312, feb 2024.
