Software Analytics Research Group at Institute of Science Tokyo (formerly Tokyo Insitute of Technology)
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Home » Articles posted by sugiyama

Author Archives: sugiyama

[Research] JISDLab: A web-based interactive literate debugging environment

  • 03
  • /
  • 17
    2022
  • sugiyama
  • Comments Off on [Research] JISDLab: A web-based interactive literate debugging environment
  • research

Mr. Sakutaro Sugiyama, a master’s student of our laboratory, presented our paper at the 29th IEEE Intern […]

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[Research] Software Defect Prediction via Multi-Channel Convolutional Neural Network

  • 12
  • /
  • 08
    2021
  • sugiyama
  • Comments Off on [Research] Software Defect Prediction via Multi-Channel Convolutional Neural Network
  • research

Chen Lang presented our paper in The 21st IEEE International Conference on Quality Software (QRS 2021). Author […]

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[Research] Characterising the Knowledge about Primitive Variables in Java Code Comments

  • 05
  • /
  • 17
    2021
  • sugiyama
  • Comments Off on [Research] Characterising the Knowledge about Primitive Variables in Java Code Comments
  • research

Dr. Mahfouth Alghamdi who visited as a junior research fellow till March 2021, presented our paper at the IEEE […]

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[Research] Untangling Composite Changes Using Tree-based Convolution Neural Network

  • 03
  • /
  • 05
    2021
  • sugiyama
  • Comments Off on [Research] Untangling Composite Changes Using Tree-based Convolution Neural Network
  • research

Cong Li, a master’s student of our laboratory, presented our paper in the 2021 March Meeting of IEICE Te […]

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Recent Posts

  • [Research] JISDLab: A web-based interactive literate debugging environment 2022/03/17
  • [Research] Software Defect Prediction via Multi-Channel Convolutional Neural Network 2021/12/08
  • [Research] Characterising the Knowledge about Primitive Variables in Java Code Comments 2021/05/17
  • [Research] Untangling Composite Changes Using Tree-based Convolution Neural Network 2021/03/05
  • [Master Thesis] Untangling Composite Commits Using Tree-based Convolution Neural Network 2021/01/25
  • [Master’s Thesis] Multi-Channel Convolutional Neural Network for Software Defect Prediction 2020/08/10
  • [Research] Toward Interaction based Evaluation of Visualization Approaches to Comprehending the Program Behavior 2019/02/24
  • Doctoral dissertation public defense (Kunihiro Noda) 22nd Jan. 2019/01/07
  • [Research] Generating an Interactive View of Dynamic Aspects of API Usage Examples 2018/09/25
  • [Research] Enriching API Documentation by Relevant API Methods Recommendation based on Version History 2018/09/25

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News

  • [Research] JISDLab: A web-based interactive literate debugging environment 2022/03/17
  • [Research] Software Defect Prediction via Multi-Channel Convolutional Neural Network 2021/12/08
  • [Research] Characterising the Knowledge about Primitive Variables in Java Code Comments 2021/05/17
  • [Research] Untangling Composite Changes Using Tree-based Convolution Neural Network 2021/03/05
  • [Master Thesis] Untangling Composite Commits Using Tree-based Convolution Neural Network 2021/01/25

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TKLAB – Software Analytics Research Group

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