Ahmed, Salman, Singh, Muskaan, Doherty, Brendan, Ramlan, Effirul, Harkin, Kathryn, Bucholc, Magda and Coyle, Damien (2023) Knowledge-based intelligent system for IT incident DevOps. In: IEEE/ACM International Conference on Software Engineering, 15th May 2023, Melbourne, Australia.

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Abstract

The automation of IT incident management (i.e., handling of any unusual events that hamper the quality of IT services) is a main focus in Artificial Intelligence for IT Operations (AIOPS). The success and reputation of large-scale firms depend on their customer service and helpdesk system. These systems tend to handle client requests and track customer service agent interactions. In this research, we present a complete knowledge-based system that automates two core components of IT incident service management (ITSM): (1) Ticket Assignment Group(TAG) and (2) Incident Resolution (IR). Our proposed system bypasses the 4 core steps of the traditional ITSM process, including data investigation, event correlation, situation room collaboration, and probable root cause. It provides immediate solutions that can save companies key performance indicator(KPIs) resources and reduce the mean time to resolution (MTTR). The experiment used an industrial, real-time ITSM dataset from a prominent IT organization comprising 500,000 real-time incident descriptions with encoded labels. Furthermore, our systems are then evaluated with an open-source dataset. Compared to the existing benchmark methodologies, there is a 5 % improvement in terms of Accuracy score. The study demonstrates AI automation capabilities in incident handling (TAG and IR) for large real- world IT systems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	computing, AI, IT incident management, Artificial Intelligence
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Other Departments (Central units) > Research & Enterprise
Depositing User:	Salman Ahmed
Date Deposited:	25 Sep 2023 07:59
Last Modified:	25 Sep 2023 07:59
URI:	https://oars.uos.ac.uk/id/eprint/3347

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