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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
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Posts
Future Blog Post
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
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Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Program Generation and Code Completion Techniques Based on Deep Learning: Literature Review
Published in journal of Software (Chinese), 2019
Recommended citation: Hu, X. (2019). "Program Generation and Code Completion Techniques Based on Deep Learning: Literature Review." journal of Software (Chinese). 2019. http://jos.org.cn/html/2019/5/5717.htm
Program Comprehension Based on Deep Learning
Published in Journal of Computer Research and Development (Chinese), 2019
Recommended citation: Liu, F. (2019). "Program Comprehension Based on Deep Learning." Journal of Computer Research and Development (Chinese). 2019. https://crad.ict.ac.cn/EN/10.7544/issn1000-1239.2019.20190185
Program Comprehension: Present and Future
Published in Journal of Software (Chinese), 2019
Recommended citation: Jin, Z. (2019). "Program Comprehension: Present and Futuret; Journal of Software (Chinese). 2019. http://jos.org.cn/html/2019/1/5643.htm
Modeling Programs Hierarchically with Stack-augmented LSTM
Published in Journal of Systems and Software (JSS), 2020
This paper proposed a neural language model aiming at modeling the hierarchical structure of the programs, which strengthens the LSTM network with a stack to store and restore the contextual information depending on the program’s structure.
Recommended citation: Liu, F. (2020). "Modeling Programs Hierarchically with Stack-augmented LSTM." Journal of Systems and Software. 2020). https://arxiv.org/pdf/2002.04516.pdf
Multi-task Learning based Pre-trained Language Model for Code Completion
Published in International Conference on Automated Software Engineering (ASE), 2020
This paper presented a pre-trained language model with a transformer-based architecture for code understanding and generation. And also utilize static type information of the identifiers to help the model understand programs better.
Recommended citation: Liu, F. (2020). "Multi-task learning based pre-trained language model for code completion." International Conference on Automated Software Engineering. 2020. https://arxiv.org/pdf/2012.14631.pdf
A Self-Attentional Neural Architecture for Code Completion
Published in International Conference on Program Comprehension (ICPC, ACM Distinguished Paper Award), 2020
This paper built a multi-task learning model for source code modeling and code completion, which predicts next node’s type and value jointly. Employed Transformer-XL network as the base model and consider the path from the predicting node to the root node.
Recommended citation: Liu, F. (2020). "A Self-Attentional Neural Architecture for Code Completion." International Conference on Program Comprehension. 2020. https://arxiv.org/pdf/1909.06983.pdf
Learning to Recommend Method Names with Global Context
Published in International Conference on Software Engineering (ICSE), 2022
Propose a novel global approach for method name suggestion, which considers the local context, the project level context, and the documentation of the method simultaneously. We employ a transformer-based seq2seq framework to generate the method names and use the attention mechanism to allow the model to attend to different level contexts when generating the names. The model substantially improves the performance of the previous approaches on suggesting Java method names.
Recommended citation: Liu, F. (2022). "Learning to Recommend Method Names with Global Context." International Conference on Software Engineering. 2022. https://arxiv.org/pdf/2201.10705.pdf
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.