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Michele Tufano

3 accepted papers

2021

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

NeurIPS 2021poster

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 learning research for program understanding and generation. CodeXGLUE includes a collection of 10 tasks across 14 datasets…

Cited by 981SourcecodeScholar
2021

GraphCodeBERT: Pre-training Code Representations with Data Flow

ICLR 2021poster

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 summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent…

2021

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

EMNLP 2021main

Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development environments. The finite context window of these neural models means, however, that…