A Risk-Integrated Perspective on the Influence of Technical and Environmental Complexity in Software Effort Estimation
Received: 27 May 2025 | Revised: 13 July 2025 | Accepted: 1 August 2025 | Online: 19 November 2025
Corresponding author: Riyanarto Sarno
Abstract
Accurate software effort estimation is a persistent challenge in project management, as a significant share of project failures is attributed to inadequate prior risk consideration. Among the widely adopted methodologies that are used for early estimation of the success of a project is the Use Case Points (UCP) method, yet its framework does not take into consideration risk factors, undermining its reliability. To improve upon this limitation, this study introduces UCPRisk, a rule-based expert-guided UCP model that explicitly integrates risk factors across six dimensions: requirements, planning and control, project complexity, user involvement, team dynamics, and organizational environment. The model was evaluated through three public service application case studies, producing effort estimates of 1,564.42 man-hours (agriculture application), 7,287.91 man-hours (company registration), and 4,669.17 man-hours (industrial permit registration). Compared to traditional UCP, UCPRisk reduced the average Relative Error (RE) to 24.3%, demonstrating improved alignment with actual project outcomes. While not a definitive cost calculation tool, UCPRisk provides a structured framework that embeds risk into early estimation, offering more realistic projections for software development.
Keywords:
use case points, software risk, risk complexity, software estimation, education qualityDownloads
References
PMI Pulse of the Profession, "Success in Disruptive Times: Expanding the Value Delivery Landscape to Address the High Cost of Low Performance," 2018. [Online]. Available: https://www.pmi.org/-/media/pmi/documents/public/pdf/learning/thought-leadership/pulse/pulse-of-the-profession-2018.pdf.
J. Menezes, C. Gusmão, and H. Moura, "Risk factors in software development projects: a systematic literature review," Software Quality Journal, vol. 27, no. 3, pp. 1149–1174, Sep. 2019. DOI: https://doi.org/10.1007/s11219-018-9427-5
P. Singal, P. Sharma, and A. C. Kumari, "Risk integrated effort estimation of software projects: a comparative analysis of machine learning techniques," International Journal of System of Systems Engineering, vol. 15, no. 2, pp. 166–195, 2025. DOI: https://doi.org/10.1504/IJSSE.2025.146198
R. Natarajan and K. Balachandran, "Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation," in Congress on Intelligent Systems, vol. 114, M. Saraswat, H. Sharma, K. Balachandran, J. H. Kim, and J. C. Bansal, Eds. Singapore: Springer Nature Singapore, 2022, pp. 635–644. DOI: https://doi.org/10.1007/978-981-16-9416-5_46
B. W. Boehm, "Software risk management: principles and practices," IEEE Software, vol. 8, no. 1, pp. 32–41, Jan. 1991. DOI: https://doi.org/10.1109/52.62930
L. Wallace, M. Keil, and A. Rai, "How Software Project Risk Affects Project Performance: An Investigation of the Dimensions of Risk and an Exploratory Model*," Decision Sciences, vol. 35, no. 2, pp. 289–321, May 2004. DOI: https://doi.org/10.1111/j.00117315.2004.02059.x
M. J. Carr, S. L. Konda, I. Monarch, F. C. Ulrich, and C. F. Walker, "Taxonomy-Based Risk Identification," Software Engineering Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, Technical CMU/SEI-93-TR-6 ESC-TR-93-183, Jun. 1993. [Online]. Available: https://www.sei.cmu.edu/documents/1077/1993_005_001_16166.pdf.
M. N. A. Khan, A. M. Mirza, and I. Saleem, "Software Risk Analysis with the use of Classification Techniques: A Review," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5678–5682, Jun. 2020. DOI: https://doi.org/10.48084/etasr.3440
A. Elzamly, B. Hussin, and N. M. Salleh, "Top Fifty Software Risk Factors and the Best Thirty Risk Management Techniques in Software Development Lifecycle for Successful Software Projects," International Journal of Hybrid Information Technology, vol. 9, no. 6, pp. 11–32, Jun. 2016. DOI: https://doi.org/10.14257/ijhit.2016.9.6.02
R. S. Dewi and Y. S. Dharmawan, "A Proposed Model for Embedding Risk Proportion in Software Development Effort Estimation," Procedia Computer Science, vol. 234, pp. 1777–1784, 2024. DOI: https://doi.org/10.1016/j.procs.2024.03.185
L. Wallace, M. Keil, and A. Rai, "Understanding software project risk: a cluster analysis," Information & Management, vol. 42, no. 1, pp. 115–125, Dec. 2004. DOI: https://doi.org/10.1016/j.im.2003.12.007
A. Trendowicz, Software Cost Estimation, Benchmarking, and Risk Assessment: The Software Decision-Makers’ Guide to Predictable Software Development. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. DOI: https://doi.org/10.1007/978-3-642-30764-5
C. Kumar and D. K. Yadav, "A Probabilistic Software Risk Assessment and Estimation Model for Software Projects," Procedia Computer Science, vol. 54, pp. 353–361, 2015. DOI: https://doi.org/10.1016/j.procs.2015.06.041
J. V. D. Menezes Júnior, "Measuring Risks in Software Development Projects," Ph.D. Thesis, Center of Informatics of Federal University of Pernambuco, Recife, Brazil, 2019.
B. Boehm et al., COCOMO II Model Definition Manual. Center for Software Engineering at the University of Southern California (USC), 2000.
B. Boehm, R. Valerdi, and A. Brown, "COCOMO suite methodology and evolution," CROSSTALK The Journal of Defense Software Engineering, pp. 20–25, Apr. 2005.
K. Langsari, R. Sarno, and S. Sholiq, "Optimizing Time and Effort Parameters of COCOMO II using Fuzzy Multi-Objective Particle Swarm Optimization," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 16, no. 5, Oct. 2018, Art. no. 2199. DOI: https://doi.org/10.12928/telkomnika.v16i5.9698
R. S. Dewi, G. F. Prassida, Sholiq, and A. P. Subriadi, "UCPabc as an integration model for software cost estimation," in 2016 2nd International Conference on Science in Information Technology (ICSITech), Balikpapan, Indonesia, Oct. 2016, pp. 187–192. DOI: https://doi.org/10.1109/ICSITech.2016.7852631
G. Karner, "Resource estimation for objectory projects," Objective Systems SF AB, Sep. 1993.
A. Subriadi, S. Sholiq, and P. A. Ningrum, "Critical review of the effort rate value in use case point method for estimating software development effort," Journal of Theoretical and Applied Information Technology, vol. 59, no. 3, pp. 735–744, Jan. 2014.
K. Kansala, "Integrating risk assessment with cost estimation," IEEE Software, vol. 14, no. 3, pp. 61–67, Jun. 1997. DOI: https://doi.org/10.1109/52.589236
M. Ochodek, J. Nawrocki, and K. Kwarciak, "Simplifying effort estimation based on Use Case Points," Information and Software Technology, vol. 53, no. 3, pp. 200–213, Mar. 2011. DOI: https://doi.org/10.1016/j.infsof.2010.10.005
J. T. M. Dhas and Midhunchakravarthy, "The Functional and Storage Risks Associated to the Size Estimation of Parallel Computing Applications," in Advances in Parallel Computing, D. J. Hemanth, T. N. Nguyen, J. Indumathi, and S. Lakshmanan, Eds. IOS Press, 2022.
P. Singal, P. Sharma, and A. C. Kumari, "Integrating software effort estimation with risk management," International Journal of System Assurance Engineering and Management, vol. 13, no. 5, pp. 2413–2428, Oct. 2022. DOI: https://doi.org/10.1007/s13198-022-01652-y
N. Ramakrishnan, H. A. Girijamma, and K. Balachandran, "Enhanced Process Model and Analysis of Risk Integration in Software effort estimation," in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, Nov. 2019, pp. 419–422. DOI: https://doi.org/10.1109/ICSSIT46314.2019.8987841
M. Jørgensen and T. Halkjelsvik, "Sequence effects in the estimation of software development effort," Journal of Systems and Software, vol. 159, Jan. 2020, Art. no. 110448. DOI: https://doi.org/10.1016/j.jss.2019.110448
A. Sousa, J. P. Faria, J. Mendes-Moreira, D. Gomes, P. C. Henriques, and R. Graça, "Applying Machine Learning to Risk Assessment in Software Projects," in Machine Learning and Principles and Practice of Knowledge Discovery in Databases, vol. 1525, M. Kamp, I. Koprinska, A. Bibal, T. Bouadi, B. Frénay, L. Galárraga, J. Oramas, L. Adilova, G. Graça, et al., Eds. Cham: Springer International Publishing, 2021, pp. 104–118. DOI: https://doi.org/10.1007/978-3-030-93733-1_7
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