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Automatic source code summarization is the task of generating natural language descriptions for source code.
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Source code analysis: A road map. In 2007 Future of Software Engineering . IEEE Computer Society, 104–119
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On the Use of Domain Terms in Source Code. In 16th IEEE International Conference on Program Comprehension (ICPC’08) . Amsterdam, The Netherlands, 113–122
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On the use of automated text summarization techniques for summarizing source code. In Reverse Engineering (WCRE), 2010 17th Working Conference on . IEEE, 35–44
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Are deep neural networks the best choice for modeling source code?. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering . ACM, 763–773
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A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes. In ACL
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Patrick Fernandes, Miltiadis Allamanis, and Marc Brockschmidt. 2018 · 2018
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How do professional developers comprehend software?. In Proceedings of the 2012 International Conference on Software Engineering (ICSE 2012) . IEEE Press, Piscataway, NJ, USA, 255–265
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Evaluating source code summarization techniques: Replication and expansion. In Program Comprehension (ICPC), 2013 IEEE 21st International Conference on . IEEE, 13–22
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Automatically mining software-based, semantically-similar words from comment-code mappings. In 2013 10th Working Conference on Mining Software Repositories (MSR) . 377–386
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Automatic generation of natural language summaries for java classes. In Program Comprehension (ICPC), 2013 IEEE 21st International Conference on . IEEE, 23–32
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Neural machine translation by jointly learning to align and translate
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
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An eye-tracking study of java programmers and application to source code summarization
Paige Rodeghero, Cheng Liu, Paul W McBurney, and Collin McMillan. 2015 · 2015
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Adapting Neural Text Classification for Improved Software Categorization. In 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) . 461–472
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Automatic Generation of Text Descriptive Comments for Code Blocks
Yuding Liang and Kenny Q. Zhu. 2018 · 2018
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2018
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Improving Automatic Source Code Summarization via Deep Reinforcement Learning. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE 2018) . Association for Computing Machinery, New York, NY, USA, 397–407
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Graph2seq: Graph to sequence learning with attention-based neural networks
Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Michael Witbrock, and Vadim Sheinin. 2018a · 2018
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Exploiting rich syntactic information for semantic parsing with graph-to-sequence model
Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Liwei Chen, and Vadim Sheinin. 2018b · 2018
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code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2019 · 2019
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A neural model for generating natural language summaries of program subroutines. In Proceedings of the 41st International Conference on Software Engineering . IEEE Press, 795–806
Alexander LeClair, Siyuan Jiang, and Collin McMillan. 2019 · 2019
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Recommendations for Datasets for Source Code Summarization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . 3931–3937
Alexander LeClair and Collin McMillan. 2019 · 2019
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Learning to Generate Comments for API-Based Code Snippets. In Software Engineering and Methodology for Emerging Domains , Zheng Li, He Jiang, Ge Li, Minghui Zhou, and Ming Li (Eds.). Springer Singapore, Singapore, 3–14
Yangyang Lu, Zelong Zhao, Ge Li, and Zhi Jin. 2019 · 2019
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A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques
Xiaotao Song, Hailong Sun, Xu Wang, and Jiafei Yan. 2019 · 2019
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Reinforcement learning based graph-to-sequence model for natural question generation
Yu Chen, Lingfei Wu, and Mohammed J Zaki. 2020 · 2020
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Summarizing source code using a neural attention model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Vol. 1. 2073–2083
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
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