Ensemble of Unsupervised Parametric and Non-Parametric Techniques to Discover Change Actions

Abstract

To support IT change automation in cloud-native deployments, we propose to discover change actions from IT change requests. Traditional approaches to discover actions rely on a pre-established set of actions. However in practice, the catalog dictionaries rarely cover a sufficient portion of the IT change requests, resulting in missed automation opportunities. To this end, supervised and semi-supervised approaches have been proposed to detect change actions, but fall short in covering new action discovery as the IT environment evolves. We propose an ensemble technique of parametric and nonparametric grammar-based methods to discover change actions from IT change requests. We evaluate our approach on an IT dataset and find our approach provides significant coverage of actions compared to baseline approaches.