What do Programmers Discuss about Deep Learning Frameworks

Abstract

Deep learning has gained tremendous traction from the developer and researcher communities. It plays an increasingly significant role in a number of application domains. Deep learning frameworks are proposed to help developers and researchers easily leverage deep learning technologies, and they attract a great number of discussions on popular platforms, i.e., Stack Overflow and GitHub. To understand and compare the insights from these two platforms, we mine the topics of interests from these two platforms. Specifically, we apply Latent Dirichlet Allocation (LDA) topic modeling techniques to derive the discussion topics related to three popular deep learning frameworks, namely, Tensorflow, PyTorch and Theano. Within each platform, we compare the topics across the three deep learning frameworks. Moreover, we make a comparison of topics between the two platforms. Our observations include 1) a wide range of topics that are discussed about the three deep learning frameworks on both platforms, and the most popular workflow stages are Model Training and Preliminary Preparation. 2) the topic distributions at the workflow level and topic category level on Tensorflow and PyTorch are always similar while the topic distribution pattern on Theano is quite different. In addition, the topic trends at the workflow level and topic category level of the three deep learning frameworks are quite different. 3) the topics at the workflow level show different trends across the two platforms. e.g., the trend of the Preliminary Preparation stage topic on Stack Overflow comes to be relatively stable after 2016, while the trend of it on GitHub shows a stronger upward trend after 2016. Besides, the Model Training stage topic still achieves the highest impact scores across two platforms. Based on the findings, we also discuss implications for practitioners and researchers.