Incident management is one of the top priorities for IT companies. Within incident management the so-called major incidents, incidents with a severe impact on the company, require emergency actions to reduce this impact. An earlier detection of these major incidents will lead to a faster resolution time and this can be achieved by using software analytics methods. These methods have been used on incident management before which faces challenges with data quality and imbalance. However, software analytics have not been applied to major incidents in particular, which is what this thesis aims to do. To gain more insight into the possibilities and challenges of automated major incident detection, a case study at ING (a large global bank) was performed. For this case study a machine learning system has been created and assessed by eight
experts through interviews. Following from these interviews, three novel challenges were identified. The first challenge is that the impact should be measured to make a more accurate prediction. The second challenge is combining multiple information sources and the third challenge is the explainability of the decision. Furthermore, two solutions to existing challenges were investigated during the creation of the machine learning system. The first being the suitability of different machine learning models for incident data, as no direct comparison is available in literature. It is shown that Logistic Regression is best suited for this use case while the Support Vector Machine and Neural Network also perform well on incident data. Finally, some findings on the
pre-processing of the incident data are reported. It is shown that assumptions in literature about automatically generated incident data being easier to use, can not always be made and that imbalanced data still remains an unsolved problem as sampling is not suited. The main contribution of this thesis are the insights and challenges in the unexplored topic of major incident detection and general recommendations for handling incident data.

This report describes the process of the Bachelorproject(TI3806) done for ‘De Energiebespaarders’, a startup in Amsterdam striving to make homes more energy efficient through accessible advice and installation of insulation or solar panels. The goal of the project was to apply machine learning to improve their system for identifying house features; windows, doors, and walls, and calculate their surface areas to improve the advice they can give to customers. The old system could produce good results, but was time-consuming to use and sensitive in regard to user input. We chose to implement a new system that makes the process automatic. In the report, our design process and our chosen implementation is described. Our new system makes use of a convolutional neural network to give pixels a label of wall(blue), window(red), door(purple), or nothing(black), without the need for the user to click the individual features beforehand. The results are promising and can save a lot of time, but the results are still inconsistent at times. Therefore, the report also contains recommendations for improvement of this new system, as De Energiebespaarders have shown interest in further developing our system.