Abstract
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.
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Notes
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A negligible amount of tickets contain also sentences (error messages) written in English.
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Acknowledgment
We thank Dr. Jeffrey Vervoort for his valuable contribution to this work carried out during hist master thesis. Finally, some of the authors’ work is partially supported by the European Commission grant no. 787061 (H2020), ANITA, European Commission grant no. 825040 (H2020), RADON, European Commission grant no. 825480 (H2020), SODALITE.
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Baresi, L., Quattrocchi, G., Tamburri, D.A., Van Den Heuvel, WJ. (2020). Automated Quality Assessment of Incident Tickets for Smart Service Continuity. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_35
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