Predicting the lifetime of pull requests in open‐source projects. Issue 6 (25th April 2021) - de Lima Júnior, Manoel Limeira; Soares, Daricélio; Plastino, Alexandre; Murta, Leonardo
Abstract: A recent survey using industrial projects has shown that providing an estimate of the lifetime of pull requests to developers helps to speed up their conclusion. Previous work has explored pull request lifetime prediction in open‐source projects using regression techniques but with a broad margin of error. The first objective of our work was to reduce the average error rate of the prediction obtained by the regression techniques so far. We performed experiments with different regression techniques and achieved a significant decrease in the mean error rate. The second objective of our work was to obtain a more effective and useful predictive model that can classify pull requests according to five discrete time intervals. We proposed new predictive attributes for the estimation of the time intervals and employed attribute selection strategies to identify subsets of attributes that could improve the predictive behavior of the classifiers. Our classification approach achieved the best accuracy in all the 20 projects evaluated in comparison with the literature. The average accuracy was of 45.28% to predict pull request lifetime, with an average normalized improvement of 14.68% in relation to the majority class and 6.49% in relation to the state‐of‐the‐art.