The Impact of Grid Search on Bug Resolution Prediction for Open-Source Software

In software engineering, maintenance effort estimation is a challenging research topic. Several empirical studies have focused on Maintenance Effort Estimation for Open Source Software (O-MEE) using Machine Learning (ML) techniques. Although Tuning the Parameters (TP) of ML techniques has proved to improve their performance, it has not yet been investigated in the context of O-MEE for bug resolution prediction. Therefore, this empirical study investigates the impact of grid search TP method, on the performance of k-Nearest Neighbor, Support Vector Machine, and Random Tree using Eclipse Platform, Eclipse JDT, and Apache datasets. A set of 18 ML classifiers were built and the results show the positive impact of the TP on the ML techniques' performance compared to the default ones.