DRACA: Decision-support for Root Cause Analysis and Change Impact Analysis
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Most companies relying on an Information Technology (IT) system for their daily operations heavily invest in its maintenance. Tools that monitor network traffic, record anomalies and keep track of the changes that occur in the system are usually used. Root cause analysis and change impact analysis are two main activities involved in the management of IT systems. Currently, there exists no universal model to guide analysts while performing these activities. Although the Information Technology Infrastructure Library (ITIL) provides a guide to the or- ganization and structure of the tools and processes used to manage IT systems, it does not provide any models that can be used to implement the required features. This thesis focuses on providing simple and effective models and processes for root cause analysis and change impact analysis through mining useful artifacts stored in a Confguration Management Database (CMDB). The CMDB contains information about the different components in a system, called Confguration Items (CIs), as well as the relationships between them. Change reports and incident reports are also stored in a CMDB. The result of our work is the Decision support for Root cause Analysis and Change impact Analysis (DRACA) framework which suggests possible root cause(s) of a problem, as well as possible CIs involved in a change set based on di erent proposed models. The contributions of this thesis are as follows: - An exploration of data repositories (CMDBs) that have not been previously attempted in the mining software repositories research community. - A causality model providing decision support for root cause analysis based on this mined data. - A process for mining historical change information to suggest CIs for future change sets based on a ranking model. Support and con dence measures are used to make the suggestions. - Empirical results from applying the proposed change impact analysis process to industrial data. Our results show that the change sets in the CMDB were highly predictive, and that with a confidence threshold of 80% and a half life of 12 months, an overall recall of 69.8% and a precision of 88.5% were achieved. - An overview of lessons learned from using a CMDB, and the observations we made while working with the CMDB.