NL4SE Reading Group

Natural Languge for Software Engineering

Spring 2018 Meetings

We meet bi-weekly on Tuesdays 9:30-10:30am in GDC 3.816.

Reading Schedule

Papers discussed during previous semesters can be found here.

Date Paper
3/27/2018 Xinyun Chen, Chang Liu, and Dawn Song. 2018. Tree-to-tree Neural Networks for Program Translation. arXiv:1802.03691 [cs.AI].
3/06/2018 Vincent J Hellendoorn and Premkumar Devanbu. 2017. Are Deep Neural Networks the Best Choice for Modeling Source Code?. In Proceedings of the International Symposium on Foundations of Software Engineering (FSE). ACM, 763–773.
2/27/2018 Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, Michael D. Ernst. 2018. NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System. LREC 2018.
2/13/2018 Pengcheng Yin and Graham Neubig. 2017. A Syntactic Neural Model for General-Purpose Code Generation. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2017).
1/30/2018 Pablo Loyola, Edison Marrese-Taylor, and Yutaka Matsuo. 2017. A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes. (ACL).

Proposed Papers

Xiaolong Li and Kristy Elizabeth Boyer. 2015. Semantic Grounding in Dialogue for Complex Problem Solving. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics and Human Language Technology (NAACL HLT), 841-850.

Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, and Percy Liang. 2017. From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood. In Association for Computational Linguistics (ACL).

Jeremy Lacomis, Alan Jaffe, Edward J. Schwartz, Claire Le Goues, and Bogdan Vasilescu. 2018. Statistical Machine Translation is a Natural Fit for Automatic Identifier Renaming in Software Source Code.

Zexuan Zhong, Jiaqi Guo, Wei Yang, Tao Xie, Jian-Guang Lou, Ting Liu, and Dongmei Zhang. 2018. Generating Regular Expressions from Natural Language Specifications: Are We There Yet?.

Osbert Bastani, Rahul Sharma, Alex Aiken, and Percy Liang. 2017. Synthesizing Program Input Grammars. Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). ACM, 2017.

Siyuan Jiang, Ameer Armaly, and Collin McMillan. 2017. Automatically Generating Commit Messages from Diffs using Neural Machine Translation. arXiv preprint arXiv:1708.09492 (2017).

Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, and Michael D Ernst. 2017. Program Synthesis from Natural Language Using Recurrent Neural Networks. Technical Report. Technical Report UW-CSE-17-03-01, University of Washington Department of Computer Science and Engineering, Seattle, WA, USA.

Maxim Rabinovich, Mitchell Stern, and Dan Klein. 2017. Abstract Syntax Networks for Code Generation and Semantic Parsing. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).

Antonio Valerio Miceli Barone and Rico Sennrich. 2017. A parallel corpus of Python functions and documentation strings for automated code documentation and code generation. arXiv preprint arXiv:1707.02275.

Illia Polosukhin and Alexander Skidanov. 2018. Neural Program Search: Solving Programming Tasks From Description and Examples. arXiv:1802.04335 [cs.AI].

Qiao Huang, Emad Shihab, Xin Xia, David Lo, and Shanping Li. 2018. Identifying self-admitted technical debt in open source projects using text mining. Empirical Software Engineering 23(1): 418-451 (2018).

Everton da S. Maldonado, Emad Shihab, and Nikolaos Tsantalis. 2017. Using Natural Language Processing to Automatically Detect Self-Admitted Technical Debt. IEEE Trans. Software Eng. 43(11): 1044-1062 (2017).

Ziyu Yao, Daniel S. Weld, Wei-Peng Chen, and Huan Sun. 2018. StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow. In Proceedings of the 27th International Conference on World Wide Web (WWW 2018).

Mohammad Raza, Sumit Gulwani, and Natasa MilicFrayling. 2015. Compositional Program Synthesis from Natural Language and Examples. In Proceedings of IJCAI.


Please send suggestions for papers you would like to discuss to