TY - GEN
T1 - Automated Quality Assessment of Incident Tickets for Smart Service Continuity
AU - Baresi, Luciano
AU - Quattrocchi, Giovanni
AU - Tamburri, Damian Andrew
AU - Heuvel, Willem-Jan van den
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Digital transformation
KW - Incident Management
KW - Natural Language Processing
KW - Service continuity
UR - http://www.scopus.com/inward/record.url?scp=85098266010&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-65310-1_35
DO - 10.1007/978-3-030-65310-1_35
M3 - Conference contribution
SN - 978-3-030-65309-5
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 492
EP - 499
BT - Service-Oriented Computing - 18th International Conference, ICSOC 2020, Proceedings
A2 - Kafeza, Eleanna
A2 - Benatallah, Boualem
A2 - Martinelli, Fabio
A2 - Hacid, Hakim
A2 - Bouguettaya, Athman
A2 - Motahari, Hamid
PB - Springer
ER -