Recovering Resolutions for Application Maintenance Incidents
Suman Deb Roy, John-John Yeung Tack Yan, Navin Budhiraja, Aeran Lim · 2016
These days IT service providers are rapidly embracing an automated services delivery model in order to keep pace with advances in technology and demanding market pressure to reduce and maintain quality. Application development and maintenance is a good example of a service system in which a sizable volume of tickets are raised everyday for different issues to get resolved with a view to deliver uninterrupted service. An issue is captured as summary on the ticket and once a ticket is resolved, the solution is also noted down on the ticket as resolution. It will be beneficial to automatically extract information from the description of tickets to improve operations like identifying critical and frequent issues, grouping of tickets based on textual content, suggesting remedial measures for them etc. In particular, the maintenance people can save a lot of effort and time if they have access to past remedial actions for similar kind of tickets raised earlier based on history data. In this work we propose an automated method based on background knowledge of tickets for recovering resolutions for fresh tickets using unsupervised learning and the traditional kNN (k-nearest neighbor) search. In absence of domain ontology we use ticket description to extract ontology which is grounded in WordNet. The experiment of our dataset shows that we are able to achieve a promising similarity match of about 48% between the suggestions and the actual resolution which shows an improvement over clustering without background knowledge.