Context-aware Code Summary Generation

Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958

Earlier work this paper cites.

The concept assignment problem in program understanding. In [1993] Proceedings Working Conference on Reverse Engineering . 27–43

T.J. Biggerstaff, B.G. Mitbander, and D. Webster. 1993 · 1993

Earlier work this paper cites.

Experimental analysis of input weight freezing in constructive neural networks. In IEEE International Conference on Neural Networks . IEEE, 511–516

T-Y Kwok and D-Y Yeung. 1993 · 1993

Earlier work this paper cites.

API documentation from source code comments: a case study of Javadoc. In Proceedings of the 17th Annual International Conference on Computer Documentation (New Orleans, Louisiana, USA) (SIGDOC ’99) . Association for Computing Machinery, New York, NY, USA, 147–153

Douglas Kramer. 1999 · 1999

Earlier work this paper cites.

Effects of context on program slicing

Jens Krinke. 2006 · 2006

Earlier work this paper cites.

Automated classification of non-functional requirements

Jane Cleland-Huang, Raffaella Settimi, Xuchang Zou, and Peter Solc. 2007 · 2007

Earlier work this paper cites.

Information needs in collocated software development teams. In 29th International Conference on Software Engineering (ICSE’07) . IEEE, 344–353

Amy J Ko, Robert DeLine, and Gina Venolia. 2007 · 2007

Earlier work this paper cites.

Supporting program comprehension with source code summarization. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 2 . 223–226

Sonia Haiduc, Jairo Aponte, and Andrian Marcus. 2010 · 2010

Earlier work this paper cites.

Towards automatically generating summary comments for java methods. In Proceedings of the IEEE/ACM international conference on Automated software engineering . ACM, 43–52

Giriprasad Sridhara, Emily Hill, Divya Muppaneni, Lori Pollock, and K Vijay-Shanker. 2010 · 2010

Earlier work this paper cites.

Stacksplorer: Call graph navigation helps increasing code maintenance efficiency. In Proceedings of the 24th annual ACM symposium on User interface software and technology . 217–224

Thorsten Karrer, Jan-Peter Krämer, Jonathan Diehl, Björn Hartmann, and Jan Borchers. 2011 · 2011

Earlier work this paper cites.

Automatically detecting and describing high level actions within methods. In Proceedings of the 33rd International Conference on Software Engineering . ACM, 101–110

Giriprasad Sridhara, Lori Pollock, and K Vijay-Shanker. 2011 · 2011

Earlier work this paper cites.

"Yours is better!" participant response bias in HCI. In Proceedings of the sigchi conference on human factors in computing systems . 1321–1330

Nicola Dell, Vidya Vaidyanathan, Indrani Medhi, Edward Cutrell, and William Thies. 2012 · 2012

Earlier work this paper cites.

How do professional developers comprehend software?. In Proceedings of the 34th International Conference on Software Engineering . IEEE Press, 255–265

Tobias Roehm, Rebecca Tiarks, Rainer Koschke, and Walid Maalej. 2012 · 2012

Earlier work this paper cites.

Evaluating source code summarization techniques: Replication and expansion. In 2013 21st International Conference on Program Comprehension (ICPC) . IEEE, 13–22

Brian P Eddy, Jeffrey A Robinson, Nicholas A Kraft, and Jeffrey C Carver. 2013 · 2013

Earlier work this paper cites.

Software effort models should be assessed via leave-one-out validation

Ekrem Kocaguneli and Tim Menzies. 2013 · 2013

Earlier work this paper cites.

On the comprehension of program comprehension

Walid Maalej, Rebecca Tiarks, Tobias Roehm, and Rainer Koschke. 2014 · 2014

Earlier work this paper cites.

Automatic documentation generation via source code summarization of method context. In Proceedings of the 22nd International Conference on Program Comprehension . ACM, 279–290

Paul W McBurney and Collin McMillan. 2014 · 2014

Earlier work this paper cites.

A practical guide to controlled experiments of software engineering tools with human participants

Amy J Ko, Thomas D LaToza, and Margaret M Burnett. 2015 · 2015

Earlier work this paper cites.

Automatic Source Code Summarization of Context for Java Methods

Paul W McBurney and Collin McMillan. 2016 · 2016

Earlier work this paper cites.

Using the Output Embedding to Improve Language Models. In Conference of the European Chapter of the Association for Computational Linguistics

Ofir Press and Lior Wolf. 2016 · 2016

Earlier work this paper cites.

An empirical comparison of model validation techniques for defect prediction models

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E Hassan, and Kenichi Matsumoto. 2016 · 2016

Earlier work this paper cites.

On-demand Developer Documentation. In 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME) . 479–483

Martin P. Robillard, Andrian Marcus, Christoph Treude, Gabriele Bavota, Oscar Chaparro, Neil Ernst, Marco Aurélio Gerosa, Michael Godfrey, Michele Lanza, Mario Linares-Vásquez, Gail C. Murphy, Laura Moreno, David Shepherd, and Edmund Wong. 2017 · 2017

Earlier work this paper cites.

A survey of machine learning for big code and naturalness

Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton. 2018 · 2018

Earlier work this paper cites.

code2seq: Generating sequences from structured representations of code

Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2019a · 2019

Earlier work this paper cites.

code2vec: Learning distributed representations of code

Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019b · 2019

Earlier work this paper cites.

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

Earlier work this paper cites.

Towards explaining the regularization effect of initial large learning rate in training neural networks

Yuanzhi Li, Colin Wei, and Tengyu Ma. 2019 · 2019

Cited alongside, same era.

A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques

Xiaotao Song, Hailong Sun, Xu Wang, and Jiafei Yan. 2019 · 2019

Cited alongside, same era.

Code generation as a dual task of code summarization

Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, and Zhi Jin. 2019 · 2019

Cited alongside, same era.

A Transformer-based Approach for Source Code Summarization

Original

Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020 · 2020

Cited alongside, same era.

Layerout: Freezing layers in deep neural networks

Kelam Goutam, S Balasubramanian, Darshan Gera, and R Raghunatha Sarma. 2020 · 2020

Cited alongside, same era.

Documentation matters: Human-centered ai system to assist data science code documentation in computational notebooks

April Yi Wang, Dakuo Wang, Jaimie Drozdal, Michael Muller, Soya Park, Justin D Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan. 2022 · 2022

Later among the works it cites.

A survey of automatic source code summarization

Chunyan Zhang, Junchao Wang, Qinglei Zhou, Ting Xu, Ke Tang, Hairen Gui, and Fudong Liu. 2022 · 2022

Later among the works it cites.

Gpt-4 technical report

Original

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023

Later among the works it cites.

Function call graph context encoding for neural source code summarization

Aakash Bansal, Zachary Eberhart, Zachary Karas, Yu Huang, and Collin McMillan. 2023 · 2023

Later among the works it cites.

READSUM: Retrieval-Augmented Adaptive Transformer for Source Code Summarization

YunSeok Choi, CheolWon Na, Hyojun Kim, and Jee-Hyong Lee. 2023 · 2023