Cost Estimation of DoD ACAT 1 Software Programs: Statistical Regression vs. Neural Networks
Document Type
Conference Proceeding
Publication Date
6-14-2026
Abstract
Accurately estimating software costs is crucial for Department of Defense (DoD) projects to avoid budget overruns and resource misallocation. This study compares the effectiveness of statistical regression techniques and neural network models for cost estimation, using 306 records with ESLOC, SLOC, and project attributes. A baseline model had an MAE of 1.35 and MSE of 2.82. The refined Ordinary Least Squares (OLS) regression model achieved an R2 of 0.58, Adjusted R2 of 0.57, PRESS R2 of 0.57, MAE of 0.85, and MSE of 1.20, focusing on key predictors such as ESLOC and development hours. In contrast, the best-performing neural network achieved an R2 of 0.54, MAE of 0.88, and MSE of 1.27, with L2 regularization reducing overfitting but lowering R2 to 0.48. The findings highlight the strengths of OLS regression in small-sample scenarios, where its transparency and reliability make it better suited to DoD cost analysis than neural networks, which often require extensive tuning and face instability. These results underscore the value of statistical methods in providing actionable insights for refining cost estimation frameworks and guiding resource management in Agile and traditional DoD environments.
Source Publication
Emerging Trends in Scientific Computing and Theoretical Computer Science
Recommended Citation
Chatterton, S.D., Wagner, T., White, E.D., Ritschel, J.D., Brown, M.J., Valentine, S.M. (2026). Cost Estimation of DoD ACAT 1 Software Programs: Statistical Regression vs. Neural Networks. In: Arabnia, H.R., Hodson, D.D., Grimaila, M.R., Wagner, T.J., Maurer, P.M., Deligiannidis, L. (eds) Emerging Trends in Scientific Computing and Theoretical Computer Science. CSCE 2025. Communications in Computer and Information Science, vol 2936. Springer, Cham. https://doi.org/10.1007/978-3-032-22211-4_2
Comments
© The Authors, under exclusive license to Springer Nature Switzerland AG 2026.
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