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A systematic mapping study of source code representation for deep learning in software engineering

  • Published:2022-06-07 Issue:4 Volume:16 Page:351-385
  • ISSN:1751-8806
  • Container-title:IET Software
  • language:en
  • Short-container-title:IET Software

Author:

Samoaa Hazem Peter1ORCID,Bayram Firas2,Salza Pasquale3,Leitner Philipp1

Affiliation:

1. Software Engineering and Interaction Design Division Chalmers | University of Gothenburg Gothenburg Sweden

2. Department of Mathematics and Computer Science Karlstad University Karlstad Sweden

3. Software Evolution & Architecture Lab University of Zurich Zurich Switzerland

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Graphics and Computer-Aided Design

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Batch Mode Deep Active Learning for Regression on Graph Data;2023 IEEE International Conference on Big Data (BigData);2023-12-15

2. Performance Prediction From Source Code Is Task and Domain Specific;2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC);2023-05

3. Adding Context to Source Code Representations for Deep Learning;2022 IEEE International Conference on Software Maintenance and Evolution (ICSME);2022-10

4. Software Engineering Code Workshop Based on B-RRT ∗ FND Algorithm for Deep Program Understanding Perspective;Journal of Sensors;2022-09-26

5. TEP-GNN: Accurate Execution Time Prediction of Functional Tests Using Graph Neural Networks;Product-Focused Software Process Improvement;2022
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