SELI: The Stacking-Blending Ensemble Learning and Interpretability Framework for Software Effort Estimation

Software Effort Estimation (SEE) is an important element in software engineering that predicts the time, cost, and human resources needed for project development. Accurate estimates play a vital role in project planning and risk control, but many existing models still struggle to balance prediction accuracy and interpretability. Machine learning and deep learning–based approaches, such as SENSE, NIVIM, Mdb+MoWE, and GGSNN, can improve accuracy, but they are complex and challenging to explain. This study proposes the Stacking-Blending Ensemble Learning and Interpretability Framework (SELI), a lightweight, adaptive SEE framework integrated with Explainable Artificial Intelligence (XAI) to address these issues. SELI uses ridge-based meta-learning and non-negative blending to improve stability across datasets and integrates interpretability methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), to provide insight into feature contributions. The results of experiments on six benchmark datasets show that SELI achieves Mean Absolute Error (MAE) values of 0.0042–0.0577 and coefficients of determination (R2) of up to 0.9939, outperforming the baseline model. XAI analysis reveals that SELI can provide consistent and transparent explanations of the factors influencing estimates. These findings indicate that SELI is an important step in developing an accurate SEE model and has great potential to support decision-making in software project management.