Predicting Developers' IDE Commands with Machine Learning

TL;DR

This paper explores using machine learning, including neural networks, to predict IDE commands during coding sessions, aiming to reduce developer interruptions and improve workflow efficiency.

Contribution

It is the first to analyze and model developer IDE command prediction using large-scale session data and machine learning techniques.

Findings

Neural network model achieved 64% prediction accuracy.

Data cleansing and event series parsing are effective for modeling.

Large dataset of 10 million events was utilized for training.

Abstract

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%.