Automatization of incident resolution

Incident management is a key IT Service Management sub process in every organization as a way to deal with the current volume of tickets created every year. Currently, the resolution process is still extremely human labor intensive. A large number of incidents are not from a new, never seen before problem, they have already been solved in the past and their respective resolution have been previously stored in an Incident Ticket System. Automation of repeatable tasks in IT is an important element of service management and can have a considerable impact in an organization. Using a large real-world database of incident tickets, this dissertation explores a method to automatically propose a suitable resolution for a new ticket using previous tickets’ resolution texts. At its core, the method uses machine learning, natural language parsing, information retrieval and mining. The proposed method explores machine learning models like SVM, Logistic Regression, some neural networks architecture and more, to predict an incident resolution category for a new ticket and a module to automatically retrieve resolution action phrases from tickets using part-of-speech pattern matching. In the experiments performed, 31% to 41% of the tickets from a test set was considered as solved by the proposed method, which considering the yearly volume of tickets represents a significant amount of manpower and resources that could be saved.A Gestão de incidentes é um subprocesso chave da Gestão de Serviços de TI em todas as organizações como uma forma de lidar com o volume atual de tickets criados todos os anos. Atualmente, o processo de resolução ainda exige muito trabalho humano. Um grande número de incidentes não são de um problema novo, nunca visto antes, eles já foram resolvidos no passado e sua respetiva resolução foi previamente armazenada em um Sistema de Ticket de Incidentes. A automação de tarefas repetíveis em TI é um elemento importante do Gestão de Serviços e pode ter um impacto considerável em uma organização. Usando um grande conjunto de dados reais de tickets de incidentes, esta dissertação explora um método para propor automaticamente uma resolução adequada para um novo ticket usando textos de resolução de tickets anteriores. Em sua essência, o método usa aprendizado de máquina, análise de linguagem natural, recuperação de informações e mineração. O método proposto explora modelos de aprendizagem automática como SVM, Regressão Logística, arquitetura de algumas redes neurais e mais, para prever uma categoria de resolução de incidentes para um novo ticket e um módulo para extrair automaticamente ações de resolução de tickets usando padrões de classes gramaticais. Nas experiências realizados, 31% a 41% dos tickets de um conjunto de testes foram considerados como resolvidos pelo método proposto, que considerando o volume anual de tickets representa uma quantidade significativa de mão de obra e recursos que poderiam ser economizados

Agile information technology service management with DevOps: An incident management case study

This research aims to investigate how DevOps culture can be applied in the incident management process. The authors believe, based on experience as practitioners, that agile software development methodologies are fair enough to be used on Incident Management process, to quickly restore the business interruption. An application management team which solves incidents and applies DevOps practices was studied. Three data collection methods were used: interviews, document analysis and observation. This research provides novel findings supported by metrics and real experience implementing DevOps practices in incident management process. The novelty of the findings brings advantages for academics, and due to the exploratory nature of this research, it extends the body of knowledge. It also provides contributions for practitioners, by showing how these practices can be applied and the result of the implementation of these practices. Directions of future work are also presented.info:eu-repo/semantics/acceptedVersio

Towards Effective Extraction and Linking of Software Mentions from User-Generated Support Tickets

Software support tickets contain short and noisy text from the customers. Software products are often represented by various surface forms and informal abbreviations. Automatically identifying software mentions from support tickets and determining the official names and versions are helpful for many downstream applications, \eg routing the support tickets to the right expert groups for support. In this work, we study the problem ofsoftware product name extraction andlinking from support tickets. We first annotate and analyze sampled tickets to understand the language patterns. Next, we design features using local, contextual, and external information sources, for extraction and linking models. In experiments, we show that linear models with the proposed features are able to deliver better and more consistent results, compared with the state-of-the-art baseline models, even on dataset with sparse labels

DevOps practices in incident management process

This research aims to investigate how DevOps culture can be applied in the incident management process to improve it. Given the exploratory approach of the research, it was performed a case study. For this case study an application management team was studied where a sample of 10 persons were interviewed. This team solves incidents and provides the necessary support to the users in their daily business tasks using DevOps practices. During this case study three data collection methods were used: semi structured interviews, document analysis and observation. This research provides novel findings about a possible relation between DevOps practices and incident management phases as well as on “why” and “how” can these practices help incident management. The results are supported by metrics, like time between releases, total of over delivered incidents solutions and releases per month, to justify how this team’s performance have increased after the implementation of DevOps practices. The novelty of the findings brings advantages for academics, and due to the exploratory nature of this research, it extends the body of knowledge. It also provides contributions for practitioners, by showing how these practices can be applied and the result of the implementation of these practices. Directions of future work are also presented.O objetivo desta pesquisa é investigar como a cultura DevOps pode ser aplicada ao processo de gestão de incidentes e como pode melhorá-lo. Dada a abordagem exploratória para esta pesquisa, foi feito um caso de estudo. O objeto de estudo para esta pesquisa, foi uma equipa de gestão aplicacional em gestão de incidentes, onde um conjunto de 10 pessoas foi entrevistado. Esta equipa resolve incidentes e fornece o suporte necessário aos utilizadores de negócio, nas suas tarefas do dia a dia, utilizando práticas DevOps. Durante a elaboração deste caso de estudo, foi feita a triangulação de três métodos de recolha de dados: entrevistas semiestruturadas, análise documental e observação. Esta pesquisa fornece novas conclusões sobre uma possível relação entre práticas de DevOps e as fases do processo de gestão de incidentes, tal como o “porquê” e o “como” estas práticas podem ajudar o processo de gestão de incidentes. São apresentados resultados, como o tempo entre entregas, total de soluções de incidentes entregues a mais do que estava planeado e o número de entregas por mês, de forma a justificar como existiu uma melhoria de desempenho desta equipa após a implementação destas práticas. As conclusões que são apresentadas nesta pesquisa trazem vantagens tanto para académicos devido à natureza exploratória deste estudo que estende o corpo de conhecimento científico. E também para profissionais, por demonstrar como aplicar estas práticas e os seus resultados após implementação. Direções para trabalho futuro são também apresentadas

Intelligent Data Mining Techniques for Automatic Service Management

Today, as more and more industries are involved in the artificial intelligence era, all business enterprises constantly explore innovative ways to expand their outreach and fulfill the high requirements from customers, with the purpose of gaining a competitive advantage in the marketplace. However, the success of a business highly relies on its IT service. Value-creating activities of a business cannot be accomplished without solid and continuous delivery of IT services especially in the increasingly intricate and specialized world. Driven by both the growing complexity of IT environments and rapidly changing business needs, service providers are urgently seeking intelligent data mining and machine learning techniques to build a cognitive ``brain in IT service management, capable of automatically understanding, reasoning and learning from operational data collected from human engineers and virtual engineers during the IT service maintenance. The ultimate goal of IT service management optimization is to maximize the automation of IT routine procedures such as problem detection, determination, and resolution. However, to fully automate the entire IT routine procedure is still a challenging task without any human intervention. In the real IT system, both the step-wise resolution descriptions and scripted resolutions are often logged with their corresponding problematic incidents, which typically contain abundant valuable human domain knowledge. Hence, modeling, gathering and utilizing the domain knowledge from IT system maintenance logs act as an extremely crucial role in IT service management optimization. To optimize the IT service management from the perspective of intelligent data mining techniques, three research directions are identified and considered to be greatly helpful for automatic service management: (1) efficiently extract and organize the domain knowledge from IT system maintenance logs; (2) online collect and update the existing domain knowledge by interactively recommending the possible resolutions; (3) automatically discover the latent relation among scripted resolutions and intelligently suggest proper scripted resolutions for IT problems. My dissertation addresses these challenges mentioned above by designing and implementing a set of intelligent data-driven solutions including (1) constructing the domain knowledge base for problem resolution inference; (2) online recommending resolution in light of the explicit hierarchical resolution categories provided by domain experts; and (3) interactively recommending resolution with the latent resolution relations learned through a collaborative filtering model