Automated Quality Assessment of Incident Tickets for Smart Service Continuity

Luciano Baresi, Giovanni Quattrocchi, Damian Andrew Tamburri, Willem-Jan van den Heuvel

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

Customer management operations, such as Incident Management (IM), are traditionally performed manually often resulting in time consuming and error-prone activities. Artificial Intelligence (AI) software systems and connected information management can help handle the discontinuities in critical business tasks. AI Incident Management (AIIM) becomes therefore a set of practices and tools to resolve incidents by means of AI-enabled organizational processes and methodologies. The software automation of AIIM could reduce unplanned interruptions of service and let customers resume their work as quick as possible. While several techniques were presented in the literature to automatically identify the problems described in incident tickets by customers, this paper focuses on the qualitative analysis of the provided descriptions and on using such analysis within the context of an AI-enabled business organizational process. When an incident ticket does not describe properly the problem, the analyst must ask the customer for additional details which could require several long-lasting interactions. This paper overviews ACQUA, an AIIM approach that uses machine-learning to automatically assess the quality of ticket descriptions with the goals of removing the need of additional communications and guiding the customers to properly describe the incident.

Originele taal-2Engels
TitelService-Oriented Computing - 18th International Conference, ICSOC 2020, Proceedings
RedacteurenEleanna Kafeza, Boualem Benatallah, Fabio Martinelli, Hakim Hacid, Athman Bouguettaya, Hamid Motahari
UitgeverijSpringer
Pagina's492-499
Aantal pagina's8
ISBN van elektronische versie978-3-030-65310-1
ISBN van geprinte versie978-3-030-65309-5
DOI's
StatusGepubliceerd - 2020

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12571 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Bibliografische nota

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