Abstract
<title>Abstract</title> <p>Modern software engineering organizations leverage multi-dimensional perfor mance indicators—Velocity, Quality, and Developer Experience (DX)—to gauge delivery health. However, establishing a quantifiable link between these engineer ing signals and financial outcomes like Cost-to-Serve (CTS) remains a significant challenge. This paper proposes the VQD-CTS Prediction Model, a comprehensive machine learning framework designed to predict CTS using a synthesis of V-Q-D metrics. Our ensemble regression model, trained on extensive synthetic enter prise data, achieved an R2 score of 0.885 with MAE of 79.93. Feature importance analysis identified code complexity, defect density, cycle time, and infrastructure cost share as dominant predictors. This framework supports organizations a scal able, data-driven approach to forecast budgetary needs and optimize engineering investments.</p>