most citedGenerating Bug-Fixes Using Pretrained Transformers

46 citations · 56 across the 7 of their papers we have counts for

collaborators

7 papers

cs.IR2021★ 1 cited

Distilling Transformers for Neural Cross-Domain Search

Colin B. Clement, Chen Wu, Dawn Drain +1

Pre-trained transformers have recently clinched top spots in the gamut of natural language tasks and pioneered solutions to software engineering tasks. Even information retrieval h…

cs.IR2021★ 2 cited

Are Neural Ranking Models Robust?

Chen Wu, Ruqing Zhang, Jiafeng Guo +2

Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking…

cs.SE2021★ 1 cited

Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix

Wenshuo Wang, Chen Wu, Liang Cheng +1

The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program ca…

cs.CL2021★ 46 cited

Generating Bug-Fixes Using Pretrained Transformers

Dawn Drain, Chen Wu, Alexey Svyatkovskiy +1

Detecting and fixing bugs are two of the most important yet frustrating parts of the software development cycle. Existing bug detection tools are based mainly on static analyzers,…

cs.IR2021★ 6 cited

Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers

Dawn Drain, Changran Hu, Chen Wu +2

We approach the important challenge of code autocompletion as an open-domain task, in which a sequence-to-sequence code generator model is enhanced with the ability to attend to re…

cs.IR2021

Learning to Truncate Ranked Lists for Information Retrieval

Chen Wu, Ruqing Zhang, Jiafeng Guo +3

Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically d…

cs.CL2020

On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond

Chen Wu, Prince Zizhuang Wang, William Yang Wang

Variational autoencoders (VAEs) combine latent variables with amortized variational inference, whose optimization usually converges into a trivial local optimum termed posterior co…