Applying auto-sklearn to Software Development Effort Estimation

Abstract

Recently, automated machine learning (AutoML), which automates pre-processing, model selection, and hyperparameter adjustment, is becoming more and more popular, and it is expected to provide both ease of model construction and high prediction accuracy. In this study, we apply AutoML to software development effort estimation and experimentally evaluate its effectiveness. In our experiments, we employed auto-sklearn, which is an AutoML library, as well as linear multiple regression, elastic net, and random forest for comparison. A comparison of the estimation accuracy of each model by the win-tie-loss method confirmed that auto-sklearn showed the same or better estimation performance than other models. We also summarize the results of analyzing the effect of search time of auto-sklearn on the estimation accuracy.