Developer targeted analytics: Supporting software development decisions with runtime information

Runtime information of deployed software has been used by business and operations units to make informed decisions under the term “analytics”. However, decisions made by software engineers in the course of evolving software have, for the most part, been based on personal belief and gut-feeling. This could be attributed to software development being, for the longest time, viewed as an activity that is detached from the notion of operating software in a production environment. In recent years, this view has been challenged with the emergence of the DevOps movement, which aim is to promote cross-functional capabilities of development and operations activities within teams. This shift in process and mindset requires analytics tools that specifically target software developers. In this research, I investigate approaches to support developers in their decision-making by incorporating runtime information in source code and provide live feedback in IDEs by predicting the impact of code changes.