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ICSE 2020
Wed 24 June - Thu 16 July 2020
Tue 7 Jul 2020 08:05 - 08:13 at Silla - I6-Empirical Studies and Requirements Chair(s): Ita Richardson

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.

Tue 7 Jul

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08:05 - 09:05
I6-Empirical Studies and RequirementsJournal First / Software Engineering in Practice / Technical Papers at Silla
Chair(s): Ita Richardson Lero - The Irish Software Research Centre and University of Limerick
08:05
8m
Talk
What do Programmers Discuss about Deep Learning FrameworksJ1
Journal First
Junxiao Han Zhejiang University, Emad Shihab Concordia University, Zhiyuan Wan Zhejiang University, Shuiguang Deng Zhejiang University, Xin Xia Monash University
08:13
12m
Talk
Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese NetworkTechnicalArtifact Available
Technical Papers
Lin Shi ISCAS, Mingzhe Xing ISCAS, Mingyang Li ISCAS, Yawen Wang ISCAS, Shoubin Li ISCAS, Qing Wang Institute of Software, Chinese Academy of Sciences
08:25
8m
Talk
Recognizing lines of code violating company-specific coding guidelines using machine learningJ1
Journal First
Miroslaw Ochodek Poznan University of Technology, Regina Hebig Chalmers University of Technology & University of Gothenburg, Wilhelm Meding Ericsson, Gert Frost Grundfos, Miroslaw Staron University of Gothenburg
08:33
12m
Talk
Context-aware In-process Crowdworker RecommendationACM SIGSOFT Distinguished Paper AwardsTechnical
Technical Papers
Junjie Wang Institute of Software, Chinese Academy of Sciences, Ye Yang Stevens institute of technology, Song Wang York University, Yuanzhe Hu Institute of Software, Chinese Academy of Sciences, Dandan Wang Institute of Software, Chinese Academy of Sciences, Qing Wang Institute of Software, Chinese Academy of Sciences
08:45
12m
Talk
Using a Context-Aware Approach to Recommend Code Reviewers: Findings from an Industrial Case StudySEIP
Software Engineering in Practice
Anton Strand Ericsson AB, Markus Gunnarsson Ericsson AB, Ricardo Britto Ericsson / Blekinge Institute of Technology, Muhammad Usman Blekinge Institute of Technology