Incorporating External Knowledge through Pre-training for Natural Language to Code Generation

2 code implementations • ACL 2020 • Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, Graham Neubig

Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents.

Code Generation Data Augmentation +1

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

1 code implementation • 5 Dec 2021 • Qibin Chen, Jeremy Lacomis, Edward J. Schwartz, Graham Neubig, Bogdan Vasilescu, Claire Le Goues

Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection.

Contrastive Learning Learning Semantic Representations +1

Detecting and Characterizing Bots that Commit Code

2 code implementations • 2 Mar 2020 • Tapajit Dey, Sara Mousavi, Eduardo Ponce, Tanner Fry, Bogdan Vasilescu, Anna Filippova, Audris Mockus

Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects.

Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow

no code implementations • 23 May 2018 • Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, Graham Neubig

For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise.

Code Summarization Retrieval +1

In-IDE Code Generation from Natural Language: Promise and Challenges

no code implementations • 27 Jan 2021 • Frank F. Xu, Bogdan Vasilescu, Graham Neubig

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs.

Code Generation Data Visualization Software Engineering

Using an LLM to Help With Code Understanding

no code implementations • 17 Jul 2023 • Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, Brad Myers

In this study, we provide a first investigation of an LLM-based conversational UI built directly in the IDE that is geared towards code understanding.

Code Generation Language Modelling +2