Closing the Feedback Loop in DevOps Through Autonomous Monitors in Operations

Abstract

DevOps represent the tight connection between development and operations. To address challenges that arise on the borderline between development and operations, we conducted a study in collaboration with a Swedish company responsible for ticket management and sales in public transportation. The aim of our study was to explore and describe the existing DevOps environment, as well as to identify how the feedback from operations can be improved, specifically with respect to the alerts sent from system operations. Our study complies with the basic principles of the design science paradigm, such as understanding and improving design solutions in the specific areas of practice. Our diagnosis, based on qualitative data collected through interviews and observations, shows that alert flooding is a challenge in the feedback loop, i.e. too much signals from operations create noise in the feedback loop. Therefore, we design a solution to improve the alert management by optimizing when to raise alerts and accordingly introducing a new element in the feedback loop, a smart filter. Moreover, we implemented a prototype of the proposed solution design and showed that a tighter relation between operations and development can be achieved, using a hybrid method which combines rule-based and unsupervised machine learning for operations data analysis.

Special Issue on New Paradigms of Software Production and Deployment

“…This approach is illustrated through its implementation in an European company operating in the global market. Closing the Feedback Loop in DevOps Through Autonomous Monitors in Operations , by [ 7 ]: the contributions brought by this paper belong to the area of artificial intelligence for IT operations. More particularly, authors describe how to use machine learning techniques to enhance the quality of the feedback going from operations to development.…”