Data-Driven Application Maintenance: Views from the Trenches
This work addresses automation gaps in application maintenance for IT services, but it is incremental as it builds on existing research with a focus on practical adoption.
The authors tackled common IT service problems like duplicate ticket identification and assignee recommendation by developing a proof-of-concept data-driven machine learning solution, but no concrete performance numbers are provided.
In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents to business processes. In the context of IT services, these problems are frequently encountered, yet there is a gap in bringing automation and optimization. Despite long-standing research around mining and analysis of software repositories, such research outputs are not adopted well in practice due to the constraints these solutions impose on the users. We discuss need for designing pragmatic solutions with low barriers to adoption and addressing right level of complexity of problems with respect to underlying business constraints and nature of data.