Insightimate: Enhancing Software Effort Estimation Accuracy Using Machine Learning Across Three Schemas (LOC/FP/UCP)

Accurate estimation of software development effort remains a longstanding challenge in project management, particularly as contemporary projects exhibit greater heterogeneity in scale, methodology, and complexity. While traditional parametric models such as COCOMO II offer interpretability, their fixed functional forms often underfit diverse modern datasets. This paper proposes a unified machine-learning–based framework designed to improve estimation accuracy across three widely used sizing schemas: Lines of Code (LOC), Function Points (FP), and Use Case Points (UCP). The framework integrates standardized preprocessing, schema-specific feature engineering, and a set of representative regression models, including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting. Using publicly available datasets collected from prior studies spanning 1993–2022, we conduct a comprehensive evaluation based on established effort-estimation metrics (MMRE, PRED(25), MAE, RMSE, and \(\:{R}^{2}\)). Experimental results show that Random Forest achieves the best overall performance (MMRE \(\:\approx\:0.647\); PRED(25) \(\:\approx\:0.395\)), substantially outperforming COCOMO II, which exhibits poor predictive accuracy on heterogeneous datasets (MMRE \(\:\approx\:2.790\); PRED(25) \(\:\approx\:0.012\)). In addition, we perform a schema-by-schema comparison to highlight the sensitivity of different models to LOC, FP, and UCP representations. The findings demonstrate that data-driven approaches generalize more effectively across diverse project contexts, offering actionable insights for practitioners seeking reliable and scalable software effort estimation.