Identification of IT Incidents for Improved Risk Analysis by Using Machine Learning

Today almost every system or service is dependent on IT systems, and failure of these systems have serious and negative effects on the society. IT incidents are critical for the society as they can stop the function of critical systems and services. Therefore, it is important to analyze these systems for potential risks before becoming dependent on them. Moreover, in a software engineering context risk analysis is an important activity for the development and operation of safe software intensive systems. However, the increased complexity and size of software-intensive systems put additional requirements on the effectiveness of the risk analysis process. This means that the risk analysis process needs to be improved and it is believed that this can be done by having an overview of already occurred IT incidents. This study investigates how difficult it is to find relevant risks from available sources and the effort required to set up such a system. It also investigates the accuracy of the found risks. In this study 58% of texts that potentially can contain information about IT incidents were correctly identified from an experiment dataset by using the presented method. It is concluded that the identifying texts about IT incidents with automated methods like the one presented in this study is possible, but it requires some effort to set up.