416 citations · 861 across the 11 of their papers we have counts for
5 papers · 1 filter
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren +19
Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine le…
Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers
Michele Tufano, Dawn Drain, Alexey Svyatkovskiy +1
Unit testing represents the foundational basis of the software testing pyramid, beneath integration and end-to-end testing. Automated software testing researchers have proposed a v…
GraphCodeBERT: Pre-training Code Representations with Data Flow
Daya Guo, Shuo Ren, Shuai Lu +15
Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarizatio…
Fast and Memory-Efficient Neural Code Completion
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi +3
Code completion is one of the most widely used features of modern integrated development environments (IDEs). While deep learning has made significant progress in the statistical p…
Pythia: AI-assisted Code Completion System
Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu +1
In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recommendations which can be used b…