A Machine Learning Approach for Estimating Overtime Allocation in Software Development Projects

Overtime planning in software projects has traditionally been approached with search-based multi-objective optimization algorithms. However, the explicit solutions produced by these algorithms often lack applicability and acceptance in the software industry due to their disregard for project managers' intuitive knowledge. This study presents a machine learning model that learns the preferred overtime allocation patterns from solutions annotated by project managers and applied to four publicly available software development projects. The model was trained using 1092 instances of annotated solutions gathered from software houses, and the Random Forest Regression (RFR) algorithm was used to estimate the PMs’ preference. The evaluation results using MAE, RMSE, and R2 revealed that RFR exhibits excellent predictive power in this domain with minimal error. RFR also outperformed the baseline regression models in all the performance measures. The proposed machine learning approach provides a reliable and effective tool for estimating project managers' preferences for overtime plans.