Natural Language for Software Engineering
The ability to translate instructions expressed in natural language directly to executable software is of considerable use in many applications such as personal assistants, as well as in making computers and automated systems more accessible to users unfamiliar with computer programming. Our work has focused on using semantic parsing and dialog to interpret English "if this then that" (IFTTT) instructions and using the evolution of comments and code in open source software repositories and a combination of NLP and program analysis methods to automate various software engineering tasks.
Publications
     [Expand to show all 18][Minimize]
Explaining Competitive-Level Programming Solutions using LLMs 2023
Jierui Li, Szymon Tworkowski, Yingying Wu, Raymond Mooney, Association of Computational Linguistics (ACL), Natural Language Reasoning and Structured Explanations Workshop (2023).
Learning Deep Semantics for Test Completion 2023
Pengyu Nie, Rahul Banerjee, Junyi Jessy Li, Raymond Mooney and Milos Gligoric, International Conference on Software Engineering (2023).
Facilitating Software Evolution through Natural Language Comments and Dialogue 2022
Sheena Panthaplackel, PhD Thesis, Department of Computer Science, UT Austin.
Impact of Evaluation Methodologies on Code Summarization 2022
Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, and Milos Gligoric, In Annual Meeting of the Association for Computational Linguistics, May 2022.
Learning to Describe Solutions for Bug Reports Based on Developer Discussions 2022
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney, In Findings of the Annual Meeting of the Association for Computational Linguistics (ACL), May 2022.
Using Developer Discussions to Guide Fixing Bugs in Software 2022
Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney, In Findings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), December 2022.
Copy That! Editing Sequences by Copying Spans 2021
Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt, In The AAAI Conference on Artificial Intelligence (AAAI), February 2021.
Deep Just-In-Time Inconsistency Detection Between Comments and Source Code 2021
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney, In The AAAI Conference on Artificial Intelligence (AAAI), Vol. arXiv:2010.01625, February 2021.
Facilitating Software Evolution through Natural Language Comments and Dialogue 2021
Sheena Panthaplackel, Ph.D. Proposal.
Associating Natural Language Comment and Source Code Entities 2020
Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney and Junyi Jessy Li, In The AAAI Conference on Artificial Intelligence (AAAI), February 2020.
Learning to Update Natural Language Comments Based on Code Changes 2020
Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, and Raymond J. Mooney, In Proceedings of the 58th Annual Conference of the Association for Computational Linguistics (ACL), July 2020.
A Framework for Writing Trigger - Action Todo Comments in Executable Format 2019
Pengyu Nie, Rishabh Rai, Junyi Jessy Li, Sarfraz Khurshid, Raymond J. Mooney, Milos Gligoric, In Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Tallinn, Estonia, August 2019. Distinguished Paper ...
AInix: An open platform for natural language interfaces to shell commands 2019
David Gros, Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
Natural Language Processing and Program Analysis for Supporting Todo Comments as Software Evolves 2018
Pengyu Nie, Junyi Jessy Li, Sarfraz Khurshid, Raymond Mooney, Milos Gligoric, In In Proceedings of the AAAI Workshop on NLP for Software Engineering, February 2018.
Dialog for Language to Code 2017
Shobhit Chaurasia and Raymond J. Mooney, In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), pp. 175-180, Taipei, Taiwan, November 2017.
Dialog for Natural Language to Code 2017
Shobhit Chaurasia, Masters Thesis, Computer Science Department, University of Texas at Austin.
Improved Semantic Parsers For If-Then Statements 2016
I. Beltagy and Chris Quirk, To Appear In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-16), Berlin, Germany 2016.
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes 2015
Chris Quirk, Raymond Mooney, and Michel Galley, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL-15), pp. 878--888, Beijing, China, July 2015.
Labs
Machine Learning