  When a developer is writing code they are usually focused and in a
state-of-mind which some refer to as flow. Breaking out of this flow can cause
the developer to lose their train of thought and have to start their thought
process from the beginning. This loss of thought can be caused by interruptions
and sometimes slow IDE interactions. Predictive functionality has been
harnessed in user applications to speed up load times, such as in Google
Chrome's browser which has a feature called "Predicting Network Actions". This
will pre-load web-pages that the user is most likely to click through. This
mitigates the interruption that load times can introduce. In this paper we seek
to make the first step towards predicting user commands in the IDE. Using the
MSR 2018 Challenge Data of over 3000 developer session and over 10 million
recorded events, we analyze and cleanse the data to be parsed into event
series, which can then be used to train a variety of machine learning models,
including a neural network, to predict user induced commands. Our highest
performing model is able to obtain a 5 cross-fold validation prediction
accuracy of 64%.
