Cataloger: Catalog Recommendation Service for IT Change Requests

Abstract

Service automation improves the efficiency of IT service management processes. Traditionally, IT change management relies on humans to submit a change request ticket or navigate a cumbersome catalog. Today, new systems are created to execute changes based on a service catalog that is linked to back-end application programming interfaces (APIs). Consequently, a user would need to identify the right API among thousands or more items, and fill in all the required parameters. This interaction is fully self-served with little assistance. We present Cataloger a novel recommendation system that enables humans to specify their change requests in natural language sentences and recommends the most appropriate APIs. Cataloger incorporates multi-step process where IT change requests are first classified into categories, tasks and actions (APIs), and then parameters are extracted from the requests. We evaluate a well-known set of machine learning techniques for classification and parameters extraction for Cataloger, and propose a novel feedback method for improved accuracy. We evaluate Cataloger on real-world data from four different clients of IBM. Our evaluation shows that the feedback approach significantly improves the accuracy of identifying categories, tasks, and actions for change requests, thereby, improving the API recommendation to users.