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

2021-02-09 · Shuai Lu, Daya Guo, Shuo Ren, JunJie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu

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 and a platform for model evaluation and comparison. CodeXGLUE also features three baseline systems, including the BERT-style, GPT-style, and Encoder-Decoder models, to make it easy for researchers to use the platform. The availability of such data and baselines can help the development and validation of new methods that can be applied to various program understanding and generation problems.

📄 PDF Abstract BibTeX arXiv:2102.04664

Code (7)

microsoft/CodeXGLUE 공식 구현 pytorch

Avmb/semantic_neq_game

deeplearnxmu/unigencoder pytorch

facebookresearch/CodeGen

kilimanj4r0/code-summarization-beyond-function-level pytorch

sberbank-ai/fusion_brain_aij2021 pytorch

yueyuel/programgen-lms-reliability pytorch

Tasks

BIG-bench Machine LearningClone DetectionCloze Test Code Completion Code Generation Code Repair Code SearchCode SummarizationCode Translation DecoderDefect DetectionDocument TranslationText-to-Code Generation

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

2021-09-17 · EMNLP 2021 11 · Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano 외

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 envi…

Code Completion Code Generation Code Summarization Language Modeling+3

CoTexT: Multi-task Learning with Code-Text Transformer

2021-05-18 · ACL (NLP4Prog) 2021 8 · Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen 외

We present CoTexT, a pre-trained, transformer-based encoder-decoder model that learns the representative context between natural language (NL) and programming language (PL). Using self-supervision, CoTexT is pre-trained …

Code Generation Code Summarization Decoder Defect Detection+2

Improving Automated Program Repair with Domain Adaptation

2022-12-21 · Armin Zirak, Hadi Hemati

Automated Program Repair (APR) is defined as the process of fixing a bug/defect in the source code, by an automated tool. APR tools have recently experienced promising results by leveraging state-of-the-art Neural Langua…

Domain Adaptation Program Repair Zero-Shot Learning

Enhancing Code LLM Training with Programmer Attention

2025-03-19 · Yifan Zhang, Chen Huang, Zachary Karas, Dung Thuy Nguyen 외

Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost of collecting eye-tracking data, there ha…

Code Summarization

ManyTypes4TypeScript: A Comprehensive TypeScript Dataset for Sequence-Based Type Inference

2022-10-17 · International Conference on Mining Software Repositories 2022 10 · Kevin Jesse, Premkumar T. Devanbu

In this paper, we present ManyTypes4TypeScript, a very large corpus for training and evaluating machine-learning models for sequence-based type inference in TypeScript. The dataset includes over 9 million type annotat…

Type prediction